Ice Mass Balance Research Articles

Utilising ARIMA models to predict the mass balance of the Antarctic ice sheet and comprehend its fluctuations

Abstract. The mass balance of the Antarctic Ice Sheet is forecasted in this study using ARIMA models, with a particular emphasis on the distinct regions of East and West Antarctica. The research endeavours to forecast the trends in ice mass balance over the next 12 years by examining data from 1992 to 2021, thereby elucidating the potential impacts of climate change on these critical regions. The ARIMA model, which is renowned for its capacity to capture and predict time series data, has identified substantial trends that indicate a persistent loss of ice mass, particularly in West Antarctica, which has experienced substantial declines in recent decades. The model's predictions suggest that, despite the potential for a reduction in the rate of loss, the overall trend is consistent with the ongoing reduction of ice mass. These results underscore the pressing necessity for ongoing research and monitoring to gain a more comprehensive understanding of the consequences of these developments. The study also emphasises the necessity of enhancing predictive models by incorporating supplementary environmental variables to provide more comprehensive insights into the future of the Antarctic Ice Sheet and its global impacts, thereby increasing their accuracy.

Quantifying the influence of snow over sea ice morphology on L-band passive microwave satellite observations in the Southern Ocean

Abstract. Antarctic snow on sea ice can contain slush, snow ice, and stratified layers, complicating satellite retrieval processes for snow depth, ice thickness, and sea ice concentration. The presence of moist and brine-wetted snow alters microwave snow emissions and modifies the energy and mass balance of sea ice. This study assesses the impact of brine-wetted snow and slush layers on L-band surface brightness temperatures (TBs) by synergizing a snow stratigraphy model (SNOWPACK) driven by atmospheric reanalysis data and the RAdiative transfer model Developed for Ice and Snow in the L-band (RADIS-L) v1.0 The updated RADIS-L v1.1 further introduces parameterizations for brine-wetted snow and slush layers over Antarctic sea ice. Our findings highlight the importance of including both brine-wetted snow and slush layers in order to accurately simulate L-band brightness temperatures, laying the groundwork for improved satellite retrievals of snow depth and ice thickness using satellite sensors such as Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP). However, biases in modelled and observed L-band brightness temperatures persist, which we attribute to small-scale sea ice heterogeneity and snow stratigraphy. Given the scarcity of comprehensive in situ snow and ice data in the Southern Ocean, ramping up observational initiatives is imperative to not only provide satellite validation datasets but also improve process-level understanding that can scale up to improving the precision of satellite snow and ice thickness retrievals.

First Red List of Ecosystems assessment of a tropical glacier ecosystem to diagnose the pathways towards imminent collapse

Abstract Tropical glaciers are rapidly disappearing, particularly in isolated mountain peaks below 5,000 m elevation. These glaciers are fundamental substrates for unique cryogenic ecosystems in high tropical environments where the ice, melting water and rocky substrate sustain microbiological communities and other meso- and macro-biota. This study uses the Red List of Ecosystems guidelines to diagnose the collapse of the tropical glacier ecosystem of the Cordillera de Mérida, Venezuela. We undertook the assessment with existing estimates of glacier ice extent, indirect historical estimates of ice mass balance and global mechanistic models of future ice mass balance. We complemented these with additional statistical analysis of trends and bioclimatic suitability modelling to calculate and predict rates of decline and relative severity of degradation in selected ecosystem indicators. The evidence suggests an extreme risk of collapse (Critically Endangered) because of a prolonged and acute reduction in ice extent and changes in climatic conditions that are leading to the complete loss of ice mass. The ice substrate has declined 90% in the last 20 years, and observed acceleration of the rate of decline suggests it will probably disappear within the next 5 years. Loss of ice substrate will trigger an immediate loss of supraglacial, englacial and subglacial biotic compartments and initiate a decades-long succession of forefield vegetation. However, ongoing inventories of native biota and monitoring of ecosystem transitions can provide valuable insights and lessons for other ecosystems facing similar risks. The Red List of Ecosystems assessment protocol provides a useful framework for comparative analysis of cryogenic ecosystems.

Annual Minimum Snow/Ice Extent Variations over Greenland since 2000: Ice Sheet, Peripheral Areas, and Relation to Ice Mass Balance

Abstract A novel satellite image processing technique was utilized to produce an annual time series of the minimum snow/ice (MSI) extent over the entire Greenland landmass for the period 2000–22. The information was derived from the Moderate Resolution Imaging Spectroradiometer 10-day clear-sky composites over the April–September period. The data products were generated from 250-m swath imagery. The annual aggregates were downscaled to a 150-m grid for consistency with data on margins of Greenland available from the Geological Survey of Denmark and Greenland (GEUS) and the Greenland Ice Mapping Project. Interannual variations in the MSI extent were derived and analyzed for each of the seven major glacier basins in Greenland split into the main ice sheet, represented by a static map, and the peripheral areas from which all variations originated. Four of the seven regions demonstrated statistically significant negative trends in the MSI extent. The entire Greenland area also showed a declining snow/ice extent although this was not statistically significant. The region-wide and peripheral snow/ice extent varied from a minimum of 1.807 × 106 km2 (1.449 × 105 km2 for peripheral areas) observed in 2012 to a maximum of 1.860 × 106 km2 (1.977 × 105 km2) observed in 2006 with an average value of 1.829 × 106 km2 (1.664 × 105 km2). The derived MSI variations showed a statistically significant correlation with the near-surface 2-m air temperature from the ERA5-Land reanalysis and Greenland ice mass balance from GEUS for all catchments, with correlation coefficients for the entire area equal to −0.74 and 0.53, respectively. The mapping of many peripheral glaciers and ice shelves included in the glaciology databases and utilized for the IPCC reporting is not always consistent with our results and requires improvement, especially in coastal areas.

The Varied Role of Atmospheric Rivers in Arctic Snow Depth Variations

AbstractThe state and fate of snow on sea ice are crucial in the mass and energy balance of sea ice. The function of atmospheric rivers (ARs) on snow depth over sea ice has not been measured thus far, limiting the understanding of the mechanism of snow depth changes. Here, the effect of ARs on snow depth changes was explored. We found that increased AR frequency is responsible for winter‐autumn snow accumulation and spring‐summer snow melting. The 2 m air temperature (T2m), rainfall, snowfall, mean net longwave radiation (NLR), mean net shortwave radiation (NSR) and cloud radiative effect (CRE) during ARs explain the changes in snow depth triggered by AR occurrence. This work helps us understand how ARs affect snow depth changes through related physical processes, promotes an understanding of climate systems and provides a theoretical basis for snow treatment in sea ice models.

Sea ice mass balance during the MOSAiC drift experiment: Results from manual ice and snow thickness gauges

Precise measurements of Arctic sea ice mass balance are necessary to understand the rapidly changing sea ice cover and its representation in climate models. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we made repeat point measurements of snow and ice thickness on primarily level first- and second-year ice (FYI, SYI) using ablation stakes and ice thickness gauges. This technique enabled us to distinguish surface and bottom (basal) melt and characterize the importance of oceanic versus atmospheric forcing. We also evaluated the time series of ice growth and melt in the context of other MOSAiC observations and historical mass balance observations from the Surface Heat Budget of the Arctic (SHEBA) campaign and the North Pole Environmental Observatory (NPEO). Despite similar freezing degree days, average ice growth at MOSAiC was greater on FYI (1.67 m) and SYI (1.23 m) than at SHEBA (1.45 m, 0.53 m), due in part to initially thinner ice and snow conditions on MOSAiC. Our estimates of effective snow thermal conductivity, which agree with SHEBA results and other MOSAiC observations, are unlikely to explain the difference. On MOSAiC, FYI grew more and faster than SYI, demonstrating a feedback loop that acts to increase ice production after multi-year ice loss. Surface melt on MOSAiC (mean of 0.50 m) was greater than at NPEO (0.18 m), with considerable spatial variability that correlated with surface albedo variability. Basal melt was relatively small (mean of 0.12 m), and higher than NPEO observations (0.07 m). Finally, we present observations showing that false bottoms reduced basal melt rates in some FYI cases, in agreement with other observations at MOSAiC. These detailed mass balance observations will allow further investigation into connections between the carefully observed surface energy budget, ocean heat fluxes, sea ice, and ecosystem at MOSAiC and during other campaigns.

Contributions of core, mantle and climatological processes to Earth’s polar motion

Earth’s spin axis slowly moves relative to the crust over time. A 120-year-long record of this polar motion from astronomical and more modern geodetic measurements displays interannual and multidecadal fluctuations of 20 to 40 milliarcseconds superimposed on a secular trend of about 3 milliarcseconds per year. Earth’s polar motion is thought to be driven by various surface and interior processes, but how these processes operate and interact to produce the observed signal remains enigmatic. Here we show that predictions made by an ensemble of physics-informed neural networks trained on measurements to capture geophysical processes can explain the main features of the observed polar motion. We find that glacial isostatic adjustment and mantle convection primarily account for the secular trend. Mass redistribution on the Earth’s surface—for example, ice melting and global changes in water storage—yields a relatively weak trend but explains about 90% of the interannual and multidecadal variations. We also find that core processes contribute to both the secular trend and fluctuations in polar motion, either due to variations in torque at the core–mantle boundary or dynamical feedback of the core in response to surface mass changes. Our findings provide constraints on core–mantle interactions for which observations are rare and global ice mass balance over the past century and suggest feedback operating between climate-related surface processes and core dynamics.

Stream hydrology controls on ice cliff evolution and survival on debris-covered glaciers

Abstract. Ice cliffs are melt hot spots that contribute disproportionately to melt on debris-covered glaciers. In this study, we investigate the impact of supraglacial stream hydrology on ice cliffs using in situ and remote sensing observations, streamflow measurements, and a conceptual geomorphic model of ice cliff backwasting applied to ice cliffs on Kennicott Glacier, Alaska. We found that 33 % of ice cliffs (accounting for 69 % of the ice cliff area) are actively influenced by streams, while half are nearer than 10 m from the nearest stream. Supraglacial streams contribute to ice cliff formation and maintenance by horizontal meandering, vertical incision, and debris transport. These processes produce an undercut lip at the ice cliff base and transport clasts up to tens of centimeters in diameter, preventing reburial of ice cliffs by debris. Stream meander morphology reminiscent of sedimentary river channel meanders and oxbow lakes produces sinuous and crescent ice cliff shapes. Stream avulsions result in rapid ice cliff collapse and local channel abandonment. Ice cliffs abandoned by streams are observed to be reburied by supraglacial debris, indicating a strong role played by streams in ice cliff persistence. We also report on a localized surge-like event at the glacier's western margin which drove the formation of ice cliffs from crevassing; these cliffs occur in sets with parallel linear morphologies contrasting with the crescent planform shape of stream-driven cliffs. The development of landscape evolution models may assist in quantifying the total net effect of these processes on steady-state ice cliff coverage and mass balance, contextualizing them with other drivers including supraglacial ponds, differential melt, ice dynamics, and collapse of englacial voids.

Snow Penetration Depth Inversion in Tibetan Plateau Based on Coherence Scattering Model with X-band Data

Abstract. The snow penetration depth is a necessary input parameter to accurately evaluate the thickness and vertical structure of snow layer, which is conducive to understanding the freeze-thaw process of ice and snow and predicting the change of ice and snow mass balance. Interferometric Synthetic Aperture Radar (InSAR) exhibits great potential in estimating snow penetration depth due to the emission of microwave band and the ability of operating under all-day and all-weather conditions. In previous studies, the penetration depth of snow was obtained based on InSAR coherence, but the external factors such as temperature, humidity, topography, dielectric constant and so on also affect the penetration dept. In order to extract the penetration depth in detail, this paper this paper adopts a uniform volume model as the basis, introduces two parameters describing the physical properties of snow: the volume ambiguity height and the volumetric decorrelation. Then, the magnitude of volumetric decorrelation is used to establish the internal relationship between the magnitude and the volume ambiguity height to estimate the snow penetration depth. In order to verify the effectiveness and feasibility of this method, a TSX/TDX InSAR pair covering southeast Tibetan Plateau in 2012 is applied to estimate the snow penetration depth. And results indicate that X-band penetration depths range mainly between −2.753 meters and −4.589 meters with an average value of −3.671 meters m and standard deviation of 0.918 meters in snowy regions on the plateau.

Using Icepack to reproduce ice mass balance buoy observations in landfast ice: improvements from the mushy-layer thermodynamics

Abstract. Icepack (v1.1.0) – the column thermodynamics model of the Community Ice CodE (CICE) version 6 – is used to assess how changing the thermodynamics from the Bitz and Lipscomb (1999) physics (hereafter BL99) to the mushy-layer physics impacts the model performance in reproducing in situ landfast ice observations from two ice mass balance (IMB) buoys co-deployed in the landfast ice close to Nain (Labrador) in February 2017. To this end, a new automated surface retrieval algorithm is used to determine the in situ ice thickness, snow depth, basal ice congelation and snow-ice formation from the measured vertical temperature profiles. Icepack simulations are run to reproduce these observations using each thermodynamics scheme, with a particular interest in how the different physics influence the representation of snow-ice formation and ice congelation. Results show that the BL99 parameterization represents well the ice congelation but underrepresents the snow-ice contribution to the ice mass balance. In particular, defining snow-ice formation based on the hydrostatic balance alone does not reproduce the negative freeboards observed for several days in the IMB observations, resulting in an earlier snow-flooding onset, a positive ice thickness bias and reduced snow depth variations. We find that the mushy-layer thermodynamics with default parameters significantly degrades the model performance, overestimating both the congelation growth and snow-ice formation. The simulated thermodynamics response to flooding, however, better represents the observations, and the best results are obtained when allowing for negative freeboards in the mushy-layer physics. We find that the mushy-layer thermodynamics produces a larger variability in congelation rates at the ice bottom interface, alternating between periods of exceedingly fast growth and periods of unrealistic basal melt. This pattern is related to persistent brine dilution in the lowest ice layer by the congelation and brine drainage parameterizations. We also show that the mushy-layer congelation parameterization produces significant frazil formation, which is not expected in a landfast ice context. This behavior is attributed to the congelation parameterization not fully accounting for the conductive heat flux imbalance at the ice–ocean boundary. We propose a modification of the mushy-layer congelation scheme that largely reduces the frazil formation and allows for better tuning of the congelation rates to match the observations. Our results demonstrate that the mushy-layer physics and its parameters can be tuned to closely match the in situ observations, although more observations are needed to better constrain them.

Quantifying Supraglacial Debris‐Related Melt‐Altering Effects on the Djankuat Glacier, Caucasus, Russian Federation

AbstractWe use a spatially distributed and physically based energy and mass balance model to derive the Østrem curve, which expresses the supraglacial debris‐related relative melt alteration versus the debris thickness, for the Djankuat Glacier, Caucasus, Russian Federation. The model is driven by meteorological data from two on‐glacier weather stations and ERA‐5 Land reanalysis data. A direct pixel‐by‐pixel comparison of the melt rates obtained from both a clean ice and debris‐covered ice mass balance model results in the quantification of debris‐related relative melt‐modification ratios, capturing the degree of melt enhancement or suppression as a function of the debris thickness. The main results show that the distinct surface features and different surface temperature/moisture and near‐surface wind regimes that persist over debris‐covered ice significantly alter the pattern of the energy and mass fluxes when compared to clean ice. Consequently, a maximum relative melt enhancement of 1.36 is modeled on the glacier for thin/patchy debris with a thickness of 0.03 m. However, insulating effects suppress sub‐debris melt under debris layers thicker than a critical debris thickness of 0.09 m. Sensitivity experiments show that especially within‐debris properties, such as the thermal conductivity and the vertical debris porosity gradient, highly impact the magnitude of the sub‐debris melt rates. Our results also highlight the scale‐dependency as well as the dynamic nature of the debris thickness‐melt relationship for changing climatic conditions, which may have significant implications for the climate change response of debris‐covered glaciers.

Sea ice cover in the Copernicus Arctic Regional Reanalysis

Abstract. The Copernicus Arctic Regional Reanalysis (CARRA) is a novel regional high-resolution atmospheric reanalysis product that covers a considerable part of the European Arctic including substantial amounts of ice-covered areas. Sea ice in CARRA is modelled by means of a one-dimensional thermodynamic sea ice parameterisation scheme, which also explicitly resolves the evolution of the snow layer over sea ice. In the present study, we assess the representation of sea ice cover in CARRA and validate it against a wide set of satellite products and observations from ice mass balance buoys. We show that CARRA adequately represents general interannual trends towards thinner and warmer ice in the Arctic. Compared to ERA5, sea ice in CARRA shows a reduced warm bias in the ice surface temperature. The strongest improvement was observed for winter months over the central Arctic and the Greenland and Barents seas where a 4.91 °C median ice surface temperature error in ERA5 is reduced to 1.88 °C in CARRA on average. Over Baffin Bay, intercomparisons suggest the presence of a cold winter-time ice surface temperature bias in CARRA. No improvement over ERA5 was found in the ice surface albedo with spring-time errors in CARRA being up to 0.08 higher on average than those in ERA5 when computed against the CLARA-A2 satellite retrieval product. Summer-time ice surface albedos are comparable in CARRA and ERA5. Sea ice thickness and snow depth in CARRA adequately resolve the annual cycle of sea ice cover in the Arctic and bring added value compared to ERA5. However, limitations of CARRA indicate potential benefits of utilising more advanced approaches for representing sea ice cover in next-generation reanalyses.

Snow thermal conductivity and conductive flux in the Central Arctic: Estimates from observations and implications for models

During the Arctic winter, the conductive heat flux through the sea ice and snow balances the radiative and turbulent heat fluxes at the surface. Snow on sea ice is a thermal insulator that reduces the magnitude of the conductive flux. The thermal conductivity of snow, that is, how readily energy is conducted, is known to vary significantly in time and space from observations, but most forecast and climate models use a constant value. This work begins with a demonstration of the importance of snow thermal conductivity in a regional coupled forecast model. Varying snow thermal conductivity impacts the magnitudes of all surface fluxes, not just conduction, and their responses to atmospheric forcing. Given the importance of snow thermal conductivity in models, we use observations from sea ice mass balance buoys installed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition to derive the profiles of thermal conductivity, density, and conductive flux. From 13 sites, median snow thermal conductivity ranges from 0.33 W m−1 K−1 to 0.47 W m−1 K−1 with a median from all data of 0.39 W m−1 K−1 from October to February. In terms of surface energy budget closure, estimated conductive fluxes are generally smaller than the net atmospheric flux by as much as 20 W m−2, but the average residual during winter is −6 W m−2, which is within the uncertainties. The spatial variability of conductive heat flux is highest during clear and cold time periods. Higher surface temperature, which often occurs during cloudy conditions, and thicker snowpacks reduce temporal and spatial variability. These relationships are compared between observations and the coupled forecast model, emphasizing both the importance and challenge of describing thermodynamic parameters of snow cover for modeling the Arctic as a coupled system.

Temperature and salinity in the Bohai Sea landfast ice: Observations and modelling

Temperature and salinity are essential thermodynamic properties of sea ice. A 40-day long field campaign was carried out in winter 2020/2021 in the northern Bohai Sea in the landfast sea ice to investigate the seasonal sea ice temperature and salinity evolution. Ice thickness was measured routinely. A thermistor string was used to measure the ice temperature. Ice samples were collected for salinity by coring (Sco) and crumbing (Scr) methods. The mean vertical values of Sco and Scr were 4.8–5.8 psu and 8.3–13.8 psu, respectively. In cold ice, Sco and Scr were very close to each other. When ice temperature increased to −5 °C, Sco and Scr tended to differ from each other, and the difference was at largest at ice bottom. The brine volume fraction (BVF) ranged between 0.03 and 0.21 for Sco and 0.03 and 0.32 for Scr. The growth of ice reached a maximum of 3.7 cm/day in very cold conditions, while the average growth rate was 0.8 cm/day comparable to other seasonally ice-covered seas. A numerical sea ice thermodynamic model was applied to simulate the ice thickness with particular attention to the effect of Sco and Scr on the ice mass balance. The modeled overall ice growth rate was 0.7 cm/day and agreed well with the observations but during cold periods the simulation was biased down (1.1 cm/day); a possible reason is underestimation of the thermal conductivity in the model. The modeled ice thickness differed by 3 cm when using Sco and Scr as the input salinity profiles indicating that salinity has a minor impact on sea ice mass balance during mid-winter.

Ice plate deformation and cracking revealed by an in situ-distributed acoustic sensing array

Abstract. Studying seismic sources and wave propagation in ice plates can provide valuable insights into understanding various processes, such as ice structure dynamics, migration, fracture mechanics and mass balance. However, the harsh environment makes it difficult to conduct in situ dense seismic observations. Consequently, our understanding of the dynamic changes within the ice sheet remains insufficient. We conducted a seismic experiment using a distributed acoustic sensing (DAS) array on a frozen lake, exciting water vibrations through underwater airgun shots. By employing an artificial intelligence method, we were able to detect seismic events that include both high-frequency icequakes and low-frequency events. The icequakes clustered along ice fractures and their activity correlated with local temperature variations. The waveforms of low-frequency events exhibit characteristics of flexural-gravity waves, which offers insights into the properties of the ice plate. Our study demonstrates the effectiveness of an DAS array as an in situ dense seismic network for investigating the internal failure process and dynamic deformation of ice plates such as the ice shelf, which may contribute to an enhanced comprehension and prediction of ice shelf disintegration.

Globally consistent estimates of high-resolution Antarctic ice mass balance and spatially resolved glacial isostatic adjustment

Abstract. A detailed understanding of how the Antarctic ice sheet (AIS) responds to a warming climate is needed because it will most likely increase the rate of global mean sea level rise. Time-variable satellite gravimetry, realized by the Gravity Recovery and Climate Experiment (GRACE) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) missions, is directly sensitive to AIS mass changes. However, gravimetric mass balances are subject to two major limitations. First, the usual correction of the glacial isostatic adjustment (GIA) effect by modelling results is a dominant source of uncertainty. Second, satellite gravimetry allows for a resolution of a few hundred kilometres only, which is insufficient to thoroughly explore causes of AIS imbalance. We have overcome both limitations by the first global inversion of data from GRACE and GRACE-FO, satellite altimetry (CryoSat-2), regional climate modelling (RACMO2), and firn densification modelling (IMAU-FDM). The inversion spatially resolves GIA in Antarctica independently from GIA modelling jointly with changes of ice mass and firn air content at 50 km resolution. We find an AIS mass balance of −144 ± 27 Gt a−1 from January 2011 to December 2020. This estimate is the same, within uncertainties, as the statistical analysis of 23 different mass balances evaluated in the Ice sheet Mass Balance Inter-comparison Exercise (IMBIE; Otosaka et al., 2023b). The co-estimated GIA corresponds to an integrated mass effect of 86 ± 21 Gt a−1 over Antarctica, and it fits better with global navigation satellite system (GNSS) results than other GIA predictions. From propagating covariances to integrals, we find a correlation coefficient of −0.97 between the AIS mass balance and the GIA estimate. Sensitivity tests with alternative input data sets lead to results within assessed uncertainties.

Ubiquitous acceleration in Greenland Ice Sheet calving from 1985 to 2022.

Nearly every glacier in Greenland has thinned or retreated over the past few decades1-4, leading to glacier acceleration, increased rates of sea-level rise and climate impacts around the globe5-9. To understand how calving-front retreat has affected the ice-mass balance of Greenland, we combine 236,328 manually derived and AI-derived observations of glacier terminus positions collected from 1985 to 2022 and generate a 120-m-resolution mask defining the ice-sheet extent every month for nearly four decades. Here we show that, since 1985, the Greenland Ice Sheet (GrIS) has lost 5,091 ± 72 km2 of area, corresponding to 1,034 ± 120 Gt of ice lost to retreat. Our results indicate that, by neglecting calving-front retreat, current consensus estimates of ice-sheet mass balance4,9 have underestimated recent mass loss from Greenland by as much as 20%. The mass loss we report has had minimal direct impact on global sea level but is sufficient to affect ocean circulation and the distribution of heat energy around the globe10-12. On seasonal timescales, Greenland loses 193 ± 25 km2 (63 ± 6 Gt) of ice to retreat each year from a maximum extent in May to a minimum between September and October. We find that multidecadal retreat is highly correlated with the magnitude of seasonal advance and retreat of each glacier, meaning that terminus-position variability on seasonal timescales can serve as an indicator of glacier sensitivity to longer-term climate change.

X- and Ku-Band SAR Backscattering Signatures of Snow-Covered Lake Ice and Sea Ice

In this work, backscattering signatures of snow-covered lake ice and sea ice from X- and Ku-band synthetic aperture radar (SAR) data are investigated. The SAR data were acquired with the ESA airborne SnowSAR sensor in winter 2012 over Lake Orajärvi in northern Finland and over landfast ice in the Bay of Bothnia of the Baltic Sea. Co-incident with the SnowSAR acquisitions, in situ snow and ice data were measured. In addition, time series of TerraSAR-X images and ice mass balance buoy data were acquired for Lake Orajärvi in 2011–2012. The main objective of our study was to investigate relationships between SAR backscattering signatures and snow depth over lake and sea ice, with the ultimate objective of assessing the feasibility of retrieval of snow characteristics using X- and Ku-band dual-polarization (VV and VH) SAR over freshwater or sea ice. This study constitutes the first comprehensive survey of snow backscattering signatures at these two combined frequencies over both lake and sea ice. For lake ice, we show that X-band VH-polarized backscattering coefficient (σo) and the Ku-band VV/VH-ratio exhibited the highest sensitivity to the snow depth. For sea ice, the highest sensitivity to the snow depth was found from the Ku-band VV-polarized σo and the Ku-band VV/VH-ratio. However, the observed relations were relatively weak, indicating that at least for the prevailing snow conditions, obtaining reliable estimates of snow depth over lake and sea ice would be challenging using only X- and Ku-band backscattering information.

Jaskinia Lodowa w Ciemniaku (Ice Cave in Ciemniak), Western Tatra, Poland - over a century-long investigations of climate warming-caused degradation of subterranean ice mass

Jaskinia Lodowa w Ciemniaku (Ice Cave in Ciemniak), in the Tatra Mountains (Tatry), is believed to host the largest subterranean ice mass in Poland. It has been known for over a century, however, the onset of its scientific investigations dates back to 1922, when Tadeusz and Stefan Zwoliński mapped it. Since then, it has become one of the best-known caves in Poland. It was described in over a hundred scientific and popular science papers. They include findings of international importance, e.g. works related to radioisotopes, ice-mass balance and age. However, some of the questions asked a century ago are still partly unanswered. One may wonder if they will be delivered before climate warming causes the largest ‘cave glacier’ in Poland to disappear.

A Simulation of Snow on Antarctic Sea Ice Based on Satellite Data and Climate Reanalyses

AbstractAlthough snow plays an important role in the energy and mass balance of sea ice, it is little studied in the Southern Ocean. We present a Lagrangian model of snow on sea ice, CASSIS, that simulates the daily creation and drift of floes. Drifting floes accumulate snow from the atmosphere and the Antarctic ice sheet, and lose snow to the ocean and snow‐ice formation. The depth of snow on Southern Ocean sea ice increases in all sectors between autumn and spring 1981–2021, reaching 40 cm in much of the Weddell Sea, coastal Amundsen Sea and south east Indian Ocean. The root mean square difference between seasonally‐averaged model and ship‐based snow depths is 13.1 cm, and between modeled and airborne snow depths from Operation IceBridge is 13.5 cm. Our model offers an alternative long‐term snow depth record to that from passive microwave (PM) radiometry, which does not capture the seasonal growth of the snow cover. We find that although the average circumpolar snow layer thickness has increased by 16 mm between 1981 and 2021 (P = 0.004), there has been a decrease of 13 mm in the Southern Pacific Ocean (P = 0.133, but significant in spring and autumn), driven by a reduction of summer sea ice extent in this region. Our model paves the way for improved satellite‐based estimates of Antarctic sea ice thickness.