Variations In Ice Thickness Research Articles

Sea Ice Thickness in the Western Ross Sea

AbstractUsing airborne measurements, we provide a first direct glimpse of the sea ice thickness distribution in the western Ross Sea, Antarctica, where the distinguishing sea ice process is the regular occurrence of the Ross Sea, McMurdo Sound, and Terra Nova Bay polynyas. Two flights in November 2017 over a length of 800 km reveal a heavily deformed ice regime with a mean thickness of 2.0 ± 1.6 m. Supported by satellite image analysis, we identify regional variability in ice thickness based on formation history. Sea ice thickness gradients are highest within 100 and 200 km of the Terra Nova Bay and McMurdo Sound polynyas, respectively, where the mean thickness of the thickest 10% of ice is 7.6 m. Overall, about 80% of the ice is heavily deformed, concentrated in ridges with thicknesses of 3.0–11.8 m. This is evidence that sea ice is much thicker than in the central Ross Sea.

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.

ВЛИЯНИЕ ДИСКРЕТИЗАЦИИ ТЕРМОДИНАМИЧЕСКОЙ МОДЕЛИ НА ВОСПРОИЗВЕДЕНИЕ ЭВОЛЮЦИИ ТОЛЩИНЫ ЛЬДА В АЗОВСКОМ МОРЕ

A nonstationary thermodynamic model simulating the seasonal variability of ice thickness and snow depth in the northeastern Sea of Azov is presented. The model considers the dependence of thermophysical characteristics of ice on its temperature and salinity. The model is numerically implemented on the grid with a variable vertical step, which allows maintaining high spatial resolution for calculating temperature profiles of ice during its formation and melting. The influence of the spatiotemporal resolution of the model on the simulation of seasonal variations in ice thickness is evaluated for the head of the Taganrog Bay in winter of 2016/17. The dependence of simulation results on the grid spacing is demonstrated. Boundary values of spatiotemporal grid steps are determined, whose further decrease does not lead to a noticeable improvement in the forecast of seasonal variations in ice thickness in the Sea of Azov and of the simulation of the temperature profile within it.

Sea Ice Properties in High-Resolution Sea Ice Models.

An Arctic sea ice-ocean model is run with three uniform horizontal resolutions (6, 4, and 2km) and identical sea ice and ocean model parameterizations, including an isotropic viscous-plastic sea ice rheology, a mechanical ice strength parameterization, and an ice ridging parameterization. Driven by the same atmospheric forcing, the three model versions all produce similar spatial patterns and temporal variations of ice thickness and motion fields, resulting in almost identical magnitude and seasonal evolution of total ice volume and mean ice concentration, ice speed, and fractions of ice of various thickness categories over the Arctic Ocean. Increasing model resolution from 6 to 2km does not significantly improve model performance when compared to NASA IceBridge ice thickness observations. This suggests that the large-scale sea ice properties of the model are insensitive to varying high resolutions within the multifloe scale (2-10km), and it may be unnecessary to adjust model parameters constantly with increasingly high resolutions. This is also true with models within the aggregate scale (10-75km), indicating that model parameters used at coarse resolution may be used at high or multiscale resolution. However, even though the three versions all yield similar mean state of sea ice, they differ in representing anisotropic properties of sea ice. While they produce a basic pattern of major sea ice leads similar to satellite observations, their leads are distributed differently in space and time. Without changing model parameters and sea ice spatiotemporal variability, the 2-km resolution model tends to capture more leads than the other two models.

Spatiotemporal variation of ice thickness of Lake Qinghai derived from field measurements and model simulation

湖冰厚度是湖泊在封冻期的重要物理参数，明晰其时空变化特征对于认识气候变暖背景下的湖冰响应规律具有重要的理论价值和现实意义.基于ERA5 Climate Reanalysis气温数据集、MODIS MOD09GQ数据产品和2019年湖冰钻孔测厚数据及雷达测厚数据，重建2000-2019年青海湖冰厚时间序列并分析其时空变化特征.结果表明：①2019年3月实测青海湖湖冰厚度平均增长速率为0.30 cm/d，高于2月份（0.12 cm/d）.基于度日法湖冰生长模型模拟的2018年11月-2019年3月青海湖冰厚平均增长速率为0.34 cm/d，与实际观测数据相比，模拟冰厚误差为±2 cm，但在河流入湖口处和湖区南侧误差较大，且冰厚模拟数值在3月中旬前高估而之后有所低估.②青海湖多年平均冰厚介于32~37 cm，其中2008-2016年湖冰厚度年际变化剧烈，呈现先增大再稳定后减小的趋势.冻结初期湖冰厚度增长迅速，12月和1月湖冰增长速率分别为0.45和0.41 cm/d，2月后冰厚增长速率放缓，2月和3月分别为0.29和0.14 cm/d.③2000-2019年冰厚整体呈现北厚南薄、东厚西薄的空间格局，多年冰厚变化幅度湖区西部较东部稳定，湖冰平均厚度与完全封冻时长及封冻期呈正相关.;Ice thickness is an important physical parameter of a lake in the freezing period. It is of great theoretical and practical significance to understand the spatiotemporal characteristics of lake ice, which helps study how lake ice responds to climate under the background of global warming. Based on the ERA5 Climate Reanalysis temperature dataset, MODIS MOD09GQ data product, and ice borehole and radar thickness measurements in 2019, the thickness of lake ice in Lake Qinghai between 2000 and 2019 was reconstructed and the spatial-temporal variation characteristic was analyzed. The results showed that the average growth rate of ice thickness in March (0.30 cm/d) was faster than that in February (0.12 cm/d) based on the field survey data. The average growth rate of ice thickness in Lake Qinghai from November 2018 to March 2019 was 0.34 cm/d simulated by the degree-day model, with the ice thickness error of ±2 cm compared with the actual observation data. However, the error was large at the entrance of the river and the south side of Lake Qinghai. Meanwhile, the simulation of ice thickness was overestimated before the mid-March but underestimated later. The annual average ice thickness of Lake Qinghai was 32-37 cm, in which, it fluctuated dramatically in 2008-2016, showing the tendency of thickening first, then stabilizing and finally thinning. At the beginning of lake freezing, the ice thickness increased rapidly with the growth rate of 0.45 cm/d in December and 0.41 cm/d in January. After February, the rate slowed down, and it was 0.29 cm/d in February and 0.14 cm/d in March. On the whole, the ice thickness of Lake Qinghai showed a spatial pattern of being thicker in the north and east, whereas being thinner in the south and west, years of spatial change were more stable in the west than in the east. The average thickness of lake ice was positively correlated with complete freezing duration and the freeze-up period.

Estimating Arctic Sea Ice Thickness with CryoSat-2 Altimetry Data Using the Least Squares Adjustment Method.

Satellite altimeters can be used to derive long-term and large-scale sea ice thickness changes. Sea ice thickness retrieval is based on measurements of freeboard, and the conversion of freeboard to thickness requires knowledge of the snow depth and snow, sea ice, and sea water densities. However, these parameters are difficult to be observed concurrently with altimeter measurements. The uncertainties in these parameters inevitably cause uncertainties in sea ice thickness estimations. This paper introduces a new method based on least squares adjustment (LSA) to estimate Arctic sea ice thickness with CryoSat-2 measurements. A model between the sea ice freeboard and thickness is established within a 5 km × 5 km grid, and the model coefficients and sea ice thickness are calculated using the LSA method. Based on the newly developed method, we are able to derive estimates of the Arctic sea ice thickness for 2010 through 2019 using CryoSat-2 altimetry data. Spatial and temporal variations of the Arctic sea ice thickness are analyzed, and comparisons between sea ice thickness estimates using the LSA method and three CryoSat-2 sea ice thickness products (Alfred Wegener Institute (AWI), Centre for Polar Observation and Modelling (CPOM), and NASA Goddard Space Flight Centre (GSFC)) are performed for the 2018–2019 Arctic sea ice growth season. The overall differences of sea ice thickness estimated in this study between AWI, CPOM, and GSFC are 0.025 ± 0.640 m, 0.143 ± 0.640 m, and −0.274 ± 0.628 m, respectively. Large differences between the LSA and three products tend to appear in areas covered with thin ice due to the limited accuracy of CryoSat-2 over thin ice. Spatiotemporally coincident Operation IceBridge (OIB) thickness values are also used for validation. Good agreement with a difference of 0.065 ± 0.187 m is found between our estimates and the OIB results.

Investigation of spatial and temporal variability of river ice phenology and thickness across Songhua River Basin, northeast China

Abstract. The regional role and trends of freshwater ice are critical factors for aquatic ecosystems, climate variability, and human activities. The ice regime has been scarcely investigated in the Songhua River Basin of northeast China. Using daily ice records of 156 hydrological stations across the region, we examined the spatial variability in the river ice phenology and river ice thickness from 2010 to 2015 and explored the role of snow depth and air temperature on the ice thickness. The river ice phenology showed a latitudinal distribution and a changing direction from southeast to northwest. We identified two spatial clusters based on Moran's I spatial autocorrelation, and results showed that the completely frozen duration with high values clustered in the Xiao Hinggan Range and that with low values clustered in the Changbai Mountains at the 95 % confidence level. The maximum ice thickness over 125 cm was distributed along the ridge of the Da Hinggan Range and Changbai Mountains, and the maximum ice thickness occurred most often in February and March. In three subbasins of the Songhua River Basin, we developed six Bayesian regression models to predict ice thickness from air temperature and snow depth. The goodness of the fit (R2) for these regression models ranged from 0.80 to 0.95, and the root mean square errors ranged from 0.08 to 0.18 m. Results showed significant and positive correlations between snow cover and ice thickness when freshwater was completely frozen. Ice thickness was influenced by the cumulative air temperature of freezing through the heat loss of ice formation and decay instead of just air temperature.

Conditions of spatiotemporal variability of the thickness of the ice cover on lakes in the Tatra Mountains

This research aimed to identify the impact of local climatic and topographic conditions on the formation and development of the ice cover in high-mountain lakes and the representativeness assessment of periodic point measurements of the ice cover thickness by taking into consideration the role of the avalanches on the icing of the lakes. Field works included measurement of the ice and snow cover thickness of seven lakes situated in the Tatra Mountains (UNESCO biosphere reserve) at the beginning and the end of the 2017/2018 winter season. In addition, morphometric, topographic and daily meteorological data of lakes from local IMGW (Polish Institute of Meteorology and Water Management) stations and satellite images were used. The obtained results enabled us to quantify the impact of the winter eolian snow accumulation on the variation in ice thickness. This variation was ranging from several centimetres up to about 2 meters and had a tendency to increase during the winter season. The thickest ice covers occurred in the most shaded places in the direct vicinity of rock walls. The obtained results confirm a dominating role of the snow cover in the variation of the ice thickness within individual lakes.

Miocene to Pleistocene glacial history of West Antarctica inferred from Nunatak geomorphology and cosmogenic-nuclide measurements on bedrock surfaces

We report geomorphic observations and cosmogenic-nuclide measurements on bedrock surfaces from three isolated nunatak groups in West Antarctica: the Pirrit Hills and Nash Hills, located in the Weddell Sea sector, and the Whitmore Mountains, located on the Ross-Weddell divide. The objectives of this paper are to (i) establish a chronology for landscape development at these sites and (ii) quantify the long-term history of ice-thickness variations in West Antarctica. These nunataks display relic alpine landscapes on which weathered bedrock surfaces are superimposed. In the Pirrit Hills, an erosional trimline is etched into alpine ridges and separates smooth-crested ridges below from serrated ridges above. Below the trimline, geomorphic evidence indicates repeated frozen-based ice cover, while above the trimline evidence for ice cover is entirely absent. There is also no geomorphic evidence for thicker-than-present ice at the Whitmore Mountains. Cosmogenic nuclide concentrations in the oldest bedrock surfaces from the Whitmore Mountains and from above the Pirrit Hills trimline indicate uninterrupted exposure for ∼12 Myr at extraordinarily low erosion rates. This places a lower limit on the timing of the formation of alpine landscapes in West Antarctica, and we hypothesize that this occurred during the relatively warm climates prior to the mid-Miocene cooling. The absence of evidence for thicker ice at the Whitmore Mountains is consistent with the hypothesis that the divide was typically thinner than present during Pleistocene glacial periods due to reduced accumulation. Bedrock surfaces below the trimline have much lower nuclide concentrations and are most easily explained by a scenario of repeated frozen-based ice cover and occasional subglacial plucking, which is consistent with geomorphic observations. Bedrock surfaces near the modern ice level appear to have been covered more than half of the time, while higher elevation surfaces indicate progressively less cover. The trimline and associated geomorphic features are very similar to a prominent trimline in the Ellsworth Mountains, and we conclude that these are, in fact, part of the same feature. The Ellsworth trimline is hypothesized to have formed during the mid-Miocene cooling and the transition from alpine to continental glaciation, and our results are consistent with this hypothesis.

Dynamics of glacier surface velocity and ice thickness for maritime glaciers in the southeastern Tibetan Plateau

Influenced by climate warming, glacial hazards become increasingly frequent after glacier mass loss in the southeastern Tibetan Plateau. Investigations of mountain glacier surface velocities and volume are of great significance. However, no information is given regarding the specific changes in ice flow to climate warming, the spatiotemporal variations of glacier surface velocity and ice thickness are still unclear. In this study, offset tracking was used to measure glacier surface velocity in the southeastern Tibetan Plateau using Landsat TM/ETM+/OLI imagery from 1990 to 2018. And then spatially-distributed glacier ice thickness was estimated from mass-balance distribution data, glacier surface velocities and the principles of ice-flow. Results show that all maritime glaciers velocity in study area experienced a clear regional slowdown from 1991 to 2018, with robust overall trends of −5.26 m a−1 dec−1 (-19.8% dec−1) and 7.16 m a−1 dec−1 (25.2% dec−1) at elevations below and above 4 600 m a.s.l., respectively. Total volume for glaciers with an area of 954.34 km2 was found to be ~ 86.64 ± 11.09 km3 in 2013, corresponding to an average ice thickness of ~ 91 ± 12 m. The average ice thickness thinned by 0.62% a−1 during 2000–2013, and an accelerated melting can be expected in the long term and then glacier volume decreases more rapidly than in the last decades.

Spatial and Temporal Variations of Arctic Sea Ice From 2002 to 2017

AbstractThe variation of polar sea ice is an indicator of polar environmental change, which plays an important role in the study of regional and global climate change. In this paper, the latest sea ice data sets from the European Space Agency Climate Change Initiative were firstly combined to comprehensively analyze the spatial and temporal variation of Arctic sea ice concentration (SIC) and sea ice thickness (SIT) from 2002 to 2017. The results show that during this period, the SIC of the Kara Sea and the Barents Sea decreases the most significantly, reaching −1.11%·year−1. The Arctic annual average sea ice extent (SIE) and sea ice area (SIA) exhibit noticeable decreasing trends, reaching −0.0592 × 106 km·year−1 and −0.0628 × 106 km·year−1, respectively. Both of Arctic SIE and SIA vary seasonally, and they reach the minimum values of 4.97 × 106 km2 and 3.96 × 106 km2 in September and the maximum values of 14.58 × 106 km2 and 13.35 × 106 km2 in March, respectively. Moreover, the Arctic SIE and SIA anomaly series from 2002 to 2017 were analyzed based on the linear regression method. It is found the most noticeable decreasing trends of the Arctic SIE and SIA are from July to October. The maximum variation of the SIE is −1.06 × 106 km2(10a)−1, and the maximum variation of the SIA is −1.09 × 106 km2(10a)−1. The Arctic SIE and SIA present the smallest decreasing trends from April to May, which are −0.19 × 106 km2(10a)−1 and −0.2 × 106 km2(10a)−1, respectively. The Arctic SIT also displays a pronounced decreasing trend with −0.015 m·year−1 in autumn and −0.018 m·year−1 in winter. The annual average SIT reaches a minimum of 1.28 m in 2013 and a maximum of 1.66 m in 2005. Particularly, the SIT of the Hudson Bay decreases the most significantly, reaching −0.051 m·year−1. Finally, the Arctic average SIT was tested by the Mann‐Kendall method. It is found that the SIT has a sudden change in 2007, from a slight increase in 2003–2007 to a significant decrease in 2007–2017. Overall, the decreasing trend of the Arctic sea ice (both SIC and SIT) is significant in the past decade. These findings can contribute to future research on polar regions and global climate change.

The Russian Arctic seas ice age composition and thickness variation in winter periods at the beginning of the 21st century

The purpose of the paper is to analyze the large-scale peculiarities of ice age composition and thickness in winter period (October–May) along the entire stripe of the Russian Arctic seas from the Novaya Zemlya Archipelago to Alaska, as well as to assess the multi-year changes taking place in early 21st century. The source of the information used for the present paper is the electronic ice charts at the end of every month, developed in the AARI from 1997 to 2018.On the average, during the period 1997–2018, from month to month, the consecutive and ordered changes of ice area of given age take place in accordance with the general ice thickness increase. In October–December the grey-white and first year thin ice (15–70 cm) prevails in the Arctic seas, in January–February — the first-year medium ice (70–120 cm), in March–May — the first-year thick ice (more than 120 cm).The period from 1997 to 2018 consists of two parts (1997–2003 and 2004–2018) principally differing from one another by the peculiarities of ice age composition. The basic content of the changes in the 21st century is the decrease of relatively thicker ice area and increase of that of relatively thinner ice.On the average, in the winter periods of 2004–2018, (1) the area of ice thinner than 70 cm (nilas, grey, grey-white and first year thin) became 12 % less than in 1997–2003, and (2) the area of ice thicker than 70 cm (first year medium, first year thick, second year and multi-year) became 18 % larger than in 1997–2003. The most significant changes regard to the second year and multi-year ice, their area in winter period decreased about 10 times. Decrease of the rate of first year ice growth is accompanied with delay of the dates when the first year ice reaches the values of 70 and 120 cm.Correspondingly, the weighted mean ice thickness also changes. On the average, in 2004–2018 ice thickness has got about 40 cm less than in 1997–2003; by the end of cold period (May), ice thickness decrease from 153 to 117 cm.The typical feature of the observed changes is an abrupt transformation from one climatic state of ice cover to another during only one hydrological year.

Rapid glacier retreat and downwasting throughout the European Alps in the early 21st century

Mountain glaciers are known to be strongly affected by global climate change. Here we compute temporally consistent changes in glacier area, surface elevation and ice mass over the entire European Alps between 2000 and 2014. We apply remote sensing techniques on an extensive database of optical and radar imagery covering 93% of the total Alpine glacier volume. Our results reveal rapid glacier retreat across the Alps (−39 km² a−1) with regionally variable ice thickness changes (−0.5 to −0.9 m a−1). The strongest downwasting is observed in the Swiss Glarus and Lepontine Alps with specific mass change rates up to −1.03 m.w.e. a−1. For the entire Alps a mass loss of 1.3 ± 0.2 Gt a−1 (2000–2014) is estimated. Compared to previous studies, our estimated mass changes are similar for the central Alps, but less negative for the lower mountain ranges. These observations provide important information for future research on various socio-economic impacts like water resource management, risk assessments and tourism.

Estimation of Sea Ice Thickness from SARAL/AltiKa in Drifting Orbit Phase

Intra and inter-annual variations in the sea ice thickness are highly sensitive indicators of climatic variations undergoing in the earth’s atmosphere and oceans. This paper describes the method of estimating sea ice thickness using radar waveforms data acquired by SARAL/Altika mission during its drifting orbit phase from July 2016 onwards yielding spatially dense data coverage. Based on statistical analysis of return echoes, classification of the surface has been carried out in three different types, viz. floe, lead and mixed. Time delay correction methods were suitably selected and implemented to make corrections in altimetric range measurements and thereby freeboard. By assuming hydrostatic equilibrium, freeboard data were converted into sea ice thickness. Results show that sea ice thickness varies from 4 to 5 m near ice shelves and 1 to 2.5 m in the marginal sea ice regions. Freeboard and sea ice thickness estimates were also validated using NASA’s Operation Ice Bridge (OIB) datasets. Freeboard measurements show very high correlation (0.97) having RMSE of 0.13. Overestimation of approximately 1–2 m observed in the sea ice thickness, which could be attributed to distance between AltiKa footprint and OIB locations. Moreover, sensitivity analysis shows that snow depth and snow density over sea ice play crucial role in the estimation of sea ice thickness.

КОНВЕКЦИЯ ВОЗДУХА В СНЕЖНОМ ПОКРОВЕ МОРСКОГО ЛЬДА

For the first time, data on stability of stationary convective filtration within infinite horizontal layer of snow covering the flat surface of floating ice is presented in this article. An analytical solution of the linearized problem was obtained with the use of the Galerkin method, and the parametric analysis of the problem was performed. It was found that the stability criteria (Rayleigh filtration numbers) obtained with consideration for the heat exchange of snow cover with the atmosphere did not exceed the known value of 4π2 for a horizontal porous layer with impermeable isothermal boundaries. As expected, the interaction with the atmosphere has the most significant impact on the critical Rayleigh numbers, while influence of variations in snow density and ice thickness and the thickness of the underlying layer of ice are small. Based on data of ice and meteorological observations made in the winter of 2015/16 in the Western part of the Laptev Sea together with calculations of the fast ice evolution, the values and temporal variability of temperature gradients and the Rayleigh numbers in the snow cover were obtained using a thermodynamic model. It was found that both, the model and observed magnitudes, exceeded their critical values determined by solving the stability problem. The conclusion is made that the convective regime of the heat transfer does really exist in the snow cover, and thus its contribution to the thermal and mass balance of sea ice during winter period should be taken into account.

The feasibility of Arctic container shipping: the economic and environmental impacts of ice thickness

An evaluation of the competitiveness of the Northern Sea Route (NSR) for container shipping services, considering ice thickness changes during the year, is presented in the present work. The variation in ice thickness has three implications. Firstly, it entails a probability of blockage in ice and reduces the number of days in which a round-trip liner service can be completed. Secondly, ice thickness impacts schedule integrity. Thirdly, it impacts costs (icebreaker fees and fuel consumption), transit time, and the amount of CO2 emitted per TEU. Accounting for these elements in a model and then in a business case, this study concludes that NSR liner services are only competitive, compared with the Suez Canal Route or the Trans-Siberian Railway Connection, for a limited period of 1.5 months per year.

Circumpolar Thin Arctic Sea Ice Thickness and Small-Scale Roughness Retrieval Using Soil Moisture and Ocean Salinity and Soil Moisture Active Passive Observations

The variations in the Arctic sea ice thickness (SIT) due to climate change have both positive and negative effects on commercial human activities, the ecosystem, and the Earth’s environment. Satellite microwave remote sensing based on microwave reflection signals reflected by the sea ice surface has been playing an essential role in monitoring and analyzing the Arctic SIT and sea ice concentration (SIC) during the past decades. Recently, passive microwave satellites incorporating an L-band radiometer, such as soil moisture and ocean salinity (SMOS) and soil moisture active passive (SMAP), have been used for analyzing sea ice characteristics, in addition to land and ocean research. In this study, we present a novel method to estimate thin SIT and sea ice roughness (SIR) using a conversion relationship between them, from the SMAP and SMOS data. Methodologically, the SMAP SIR is retrieved. The SMAP thin SIT and SMOS SIR are estimated using a conversion relationship between thin SIT data from SMOS data and SMAP-derived SIR, which is obtained from the spatial and temporal collocation of the SMOS thin SIT and the SIR retrieved from SMAP. Our results for the Arctic sea ice during December for four consecutive years from 2015 to 2018, show high accuracy (bias = −2.268 cm, root mean square error (RMSE) = 15.919 cm, and correlation coefficient (CC) = 0.414) between the SMOS-provided thin SIT and SMAP-derived SIT, and good agreement (bias = 0.03 cm, RMSE = 0.228 cm, and CC = 0.496) between the SMOS-estimated SIR and SMAP-retrieved SIR. Consequently, our study could be effectively used for monitoring and analyzing the variation in the Arctic sea ice.

Spatial distribution of decadal ice-thickness change and glacier stored water loss in the\xa0Upper Ganga basin, India during 2000\u20132014

Himalayan glaciers have long been the focus of glaciologists across the world while trying to understand the contrasting patterns of elevation and mass changes. However, with limited number of ground observations, a comprehensive assessment of mass balance on a regional scale still remains elusive. Using the synoptic coverage of remote sensing data, we estimate a detailed spatial variation of glacier ice thickness change in the Central Himalaya of Uttarakhand using geodetic method, on a catchment scale. High resolution TerraSAR-X/TanDEM-X (12 m) and SRTM (30 m) digital elevation models (DEMs) have been utilized. The mean elevation change in the catchments is found to be −9.56 ± 0.2 m (mean annual elevation change rate is −0.68 ± 0.01 m a−1). To highlight the water potential of this region, the total ice mass loss has been estimated to be 16.0 ± 1.2 Gigatonne (Gt) from 2000–2014 from eight identified catchments namely Yamunotri, Bhagirathi, Mandakini, Alaknanda, Dhauliganga, Pindar, Goriganga and Kali/Sarda. The estimated mass balance has been validated using reported observations on five selective glaciers and the coefficient of determination is 0.93. This spatial variation of ice thickness estimated in the eight catchments is critical, as the melt-water from these glaciers contribute to the upper Ganga basin.

Results of the expedition to the North Pole on the icebreaker “50 let pobedy”

The article presents the results of the expeditionary investigations carried out from the 50 Let Pobedy nuclear icebreaker to research the present physiographic conditions of the Barents Sea and the Arctic Ocean and estimations of the main features that may reflect the global climatic variability of the seas and oceans of the Arctic. The observations of the ice lanes, hummocks, puddles, icebergs, the thickness and closeness of ice allowed us to make a real picture of the ice cover in the second half of August 2017. The zonal distribution of drifting ice, air temperature, temperature and salinity of seawater is shown. The results of observations and conclusions presented in the article reflect the simultaneous situation of the most western part of the Central Arctic adjacent to the Norwegian-Greenland basin. The analysis of new data of the structure and distribution of sea ice and icebergs gives a reason to present a number of general conclusions about the variability of ice thickness and closeness, relative age, the ratio of the area covered with ice and pure water. Recommendations about the development of new satellite systems for a reliable assessment of the seasonal and inter-annual dynamics of the area and thickness of sea ice in the Arctic Ocean are presented.

Spatial and temporal variations in basal melting at Nivlisen ice shelf, East Antarctica, derived from phase-sensitive radars

Abstract. Thinning rates of ice shelves vary widely around Antarctica, and basal melting is a major component of ice shelf mass loss. In this study, we present records of basal melting at a unique spatial and temporal resolution for East Antarctica, derived from autonomous phase-sensitive radars. These records show spatial and temporal variations of basal melting in 2017 and 2018 at Nivlisen, an ice shelf in central Dronning Maud Land. The annually averaged basal melt rates are in general moderate (∼0.8 m yr−1). Radar profiling of the ice shelf shows variable ice thickness from smooth beds to basal crevasses and channels. The highest basal melt rates (3.9 m yr−1) were observed close to a grounded feature near the ice shelf front. Daily time-varying measurements reveal a seasonal melt signal 4 km from the ice shelf front, at an ice draft of 130 m, where the highest daily basal melt rates occurred in summer (up to 5.6 m yr−1). In comparison with wind, air temperatures, and sea ice cover from reanalysis and satellite data, the seasonality in basal melt rates indicates that summer-warmed ocean surface water was pushed by wind beneath the ice shelf front. We observed a different melt regime 35 km into the ice shelf cavity, at an ice draft of 280 m, with considerably lower basal melt rates (annual average of 0.4 m yr−1) and no seasonality. We conclude that warm deep-ocean water at present has a limited effect on the basal melting of Nivlisen. On the other hand, a warming in surface waters, as a result of diminishing sea ice cover, has the potential to increase basal melting near the ice shelf front. Continuous in situ monitoring of Antarctic ice shelves is needed to understand the complex mechanisms involved in ice shelf–ocean interactions.