Ice Observations Research Articles

Expanded Understanding of the Western Antarctic Peninsula Sea‐Ice Environment Through Local and Regional Observations at Palmer Station

AbstractThe Western Antarctic Peninsula (WAP) has been experiencing rapid regional warming since at least the 1950s, however, the impacts of this warming at the local scale are variable and nuanced. Previous studies that have linked sea‐ice variability to biogeochemical cycles and food web dynamics often combine local‐scale biogeochemical data with coarse‐resolution regional satellite sea‐ice data, which may not adequately capture local sea‐ice conditions. In this study, we analyzed local‐scale in situ sea‐ice observations collected as part of a 28‐year record (1992–2020) from the Palmer Long‐Term Ecological Research site at Anvers Island, mid‐WAP, in conjunction with isotopically‐derived sea‐ice meltwater (SIM) fractions and satellite‐derived sea‐ice motion and concentration, to quantify the variability and long‐term trends in local sea‐ice behavior. In situ sea ice observations at Palmer Station displayed higher variability than satellite observations and showed no significant declines over this time, despite region‐wide declines identified in prior studies. Higher spring SIM fractions were attributed to strong northward sea‐ice motion throughout the winter. Applying these local‐scale sea‐ice insights to similarly scaled stratification and chlorophyll‐a measurements, we found that a longer‐lasting, more consistent sea‐ice pack led to greater water column stratification following the spring sea‐ice retreat. Greater sea‐ice persistence and stronger stratification led to larger peaks in chlorophyll‐a, though sea‐ice metrics did not explain the positive temporal trends in either stratification strength or chlorophyll‐a. Through this study, we identify how local sea‐ice observations and meltwater data can enhance satellite data to build an understanding of the intricate connections between ice, water column dynamics, and phytoplankton.

Calibration of Arctic ice core bromine enrichment records for past sea ice reconstructions

Bromine in ice cores has been proposed as a qualitative sea ice proxy to produce sea ice reconstructions for the polar regions. Here we report the first statistical validation of this proxy with satellite sea ice observations by combining bromine enrichment (with respect to seawater, Brenr) records from three Greenlandic ice cores (SIGMA-A, NU and RECAP) with satellite sea ice imagery, over three decades. We find that during the 1984–2016 satellite-era, ice core Brenr values are significantly correlated with first-year sea ice formed in the Baffin Bay and Labrador Sea supporting that the gas-phase bromine enrichment processes, preferentially occurring over the sea ice surface, are the main driver for the Brenr signal in ice cores. Moreover, in assessing Brenr's capability to record historical sea ice variability, we compare 20th-century Arctic Sea ice historical and proxy records with our reconstructions, based on an autoregressive–moving-average (ARMA) model, finding overall good agreement. While further enhancements are warranted, including site-specific calibrations and a comprehensive investigation into bromine transport-related concerns, this study presents a new method to quantitatively reconstruct past seasonal sea ice variability through bromine enrichment in ice cores.

Field trial of a seismoacoustic method for ice cover parameters monitoring on the Franz Josef Land archipelago

Among the physical parameters of the freezing seas ice cover, ice thickness is of key importance, and its measurement is one of the most important tasks. The increased interest in the state of the sea ice cover as an indicator of global climatic changes, as well as the growth of comprehensive development of the Arctic shelf has caused intensive development of technical and methodological bases for ice observations. Despite the great variety of approaches to ice thickness estimation, all of them are not without weaknesses. Thus, most contact methods imply direct human presence, which significantly complicates the procedure, taking into account, among other factors, the rough weather conditions of the Arctic. Remote methods depend on weather conditions and cannot always provide high spatial resolution. In this connection, it is promising to use satellite observations coupled with the results of autonomous “ground” measurements, which can be seismoacoustic data containing information on the characteristics of elastic waves propagating in the ice-covered sea, is promising. The purpose of this work is to experimentally test a new passive method for monitoring ice cover parameters along long profiles based on the analysis of natural seismoacoustic fields. The article analyzes the data of a full-scale seismoacoustic experiment with a multichannel group of geophones placed on the floating ice of Alexandra Island in the Franz Josef Land archipelago within the framework of a complex expedition of the Russian Geographical Society. The demonstrates that it is in principle possible to use flexural-gravity waves propagating in the floating ice to estimate its characteristics, both in the active mode and by analyzing the ambient noise, is demonstrated. The results of ice parameter reconstruction obtained in a nondestructive manner using seismoacoustic waves and averaged over long profiles are compared with the data of direct contact measurements. This can be further used for monitoring seasonal and multiyear variability of sea ice thickness of freezing seas, including shelf zones.

Retrieving compressive sea ice strength in the Beaufort Sea using the inverse visco-plastic model

We apply a simplified 2d Visco-Plastic (VP) sea ice model with a spatially variable representation of the sea ice rheological parameters for retrieving maximum compressive sea ice strength from satellite and in situ observations. A set of Observing System Simulation Experiments (OSSEs) demonstrates feasibility of optimizing rheological parameter of the VP sea ice model through the variational data assimilation approach during the periods of strong sea ice convergence if accurate sea ice observations are available. Following this strategy, the developed variational data assimilation VP model was applied to the sea ice velocity (https://nsidc.org/data/nsidc-0116/versions/4), sea ice concentration (https://nsidc.org/data/) and CryoSat-2 sea ice thickness observations collected in the vicinity of three moorings in the Beaufort Sea during periods of intensive sea ice convergence. Ice velocities from moorings and atmospheric wind speed (NCEP-NCAR) were used as well. Our results show that conventional maximum compressive sea ice strength (Hibler, 1979) may depend on sea ice thickness or other parameters partly controlled by the sea ice thickness, which is driven by the seasonal cycle.

Review of River Ice Observation and Data Analysis Technologies

This paper provides a comprehensive review of the available literature on the observation and characterization of river ice using remote sensing technologies. Through an analysis of 200 publications spanning from 1919 to June 2024, we reviewed different observation technologies deployed on in situ, aerial and satellite platforms for their utility in monitoring and characterizing river ice covers. River ice information, captured by 51 terms extracted from the literature, holds significant value in enhancing infrastructure resilience in the face of climate change. Satellite technologies, in particular the multispectral optical and multi-polarimetric synthetic aperture radar (SAR), provide a number of advantages, such as ice features discrimination, better ice characterization, and reliable delineation of open water and ice, with both current and upcoming sensors. The review includes data analysis methods employed for the monitoring and characterization of river ice, including ice information retrieval methods and corresponding accuracies. The need for further research on artificial intelligence and, in particular, deep learning (DL) techniques has been recognized as valuable for enhancing the accuracy of automated systems. The growing availability of freely available and commercial satellites, UAVs, and in situ data with improved characteristics suggests significant operational potential for river ice observation in the near future. Our study also identifies gaps in the current capabilities for river ice observation and provides suggestions for improved data analysis and interpretation.

Turbulent Ice–Ocean Boundary Layers in the Well-Mixed Regime: Insights from Direct Numerical Simulations

Abstract The meltwater mixing line (MML) model provides a theoretical prediction of near-ice water mass properties that is useful to compare with observations. If oceanographic measurements reported in a temperature–salinity diagram overlap with the MML prediction, then it is usually concluded that the local dynamics are dominated by the turbulent mixing of an ambient water mass with near-ice meltwater. While the MML model is consistent with numerous observations, it is built on an assumption that is difficult to test with field measurements, especially near the ice boundary, namely, that the effective (turbulent and molecular) salt and temperature diffusivities are equal. In this paper, this assumption is tested via direct numerical simulations of a canonical model for externally forced ice–ocean boundary layers in a uniform ambient. We focus on the well-mixed regime by considering an ambient temperature close to freezing and run the simulations until a statistical steady state is reached. The results validate the assumption of equal effective diffusivities across most of the boundary layer. Importantly, the validity of the MML model implies a linear correlation between the mean salinity and temperature profiles normal to the interface that can be leveraged to construct a reduced ice–ocean boundary layer model based on a single scalar variable called thermal driving. We demonstrate that the bulk dynamics predicted by the reduced thermal driving model are in good agreement with the bulk dynamics predicted by the full temperature–salinity model. Then, we show how the results from the thermal driving model can be used to estimate the interfacial heat and salt fluxes and the melt rate. Significance Statement We investigate the turbulent dynamics and thermodynamical properties of water masses below ice shelves using new data from high-resolution simulations. This is important because there are currently too few observations of ice–ocean boundary layer dynamics to construct reliable models of ice-shelf melting as a function of ocean conditions. Our results demonstrate that the turbulent diffusivities of salt and temperature are approximately equal in the well-mixed regime. This implies a linear correlation between the mean temperature and salinity profiles, consistent with the meltwater mixing line prediction that is often used to interpret polar observations. We take advantage of this correlation to propose a reduced model of ice–ocean boundary layers that can predict ice-shelf melt rates at relatively low computational cost.

Information on operational sea ice products and current and future activities of the German ice service

The ice service at the German Federal Maritime and Hydrographic Agency and its predecessors has been committed to the safety and easiness of ship navigation for more than 100 years. Within this paper, an overview of the operational products issued by the German ice service on a daily to weekly basis throughout the northern hemisphere winter season is given. These comprise written reports, ice charts, NAVTEX messages, and messages via the Global Telecommunication System to inform about the sea ice situation in German coastal waters, the Baltic Sea, and worldwide. Furthermore, the ice service has systematically collected ice observation data along the German North Sea and Baltic Sea coast since the winter of 1896/1897. The history of the German ice service is presented to put the sea ice data into context of the observation technologies used in the course of the existence of the ice service. These long-term observations enable climatological analyses of the sea ice cover in German coastal waters necessary for the safe operation of offshore infrastructure. An evaluation of the data shows a recent decline in sea ice occurrence in the Baltic Sea and German Bight. Current work is ongoing to preserve more of the historic data in digital form and also to transform the products to conform to modern standards in digital technologies and interactive solutions for the customers.

Reconstruction of Arctic Sea Ice Thickness and Its Impact on Sea Ice Forecasting in the Melting Season

Abstract Generally, sea ice prediction skills can be improved by assimilating available observations of the sea ice concentration (SIC) and sea ice thickness (SIT) into a numerical forecast model to update the initial conditions. However, due to inadequate daily SIT satellite observations in the Arctic melting season, the SIC fields in forecast models are usually directly updated, which causes mismatch of SIC and SIT in dynamics and affects the model prediction accuracy. In this study, a statistically based bivariate regression model of SIT (BRMT) is tentatively established based on the grid reanalysis data of SIC and SIT to reconstruct daily Arctic SIT data. The results show that the BRMT can reproduce the spatial and temporal changes in the SIT in the melting season and capture the variation trend of SIT in some periods. Compared with the SIT observations from buoy and satellite, the reconstructed SIT shows better performance in the central Arctic than other datasets. Furthermore, when the reconstructed SIT is added to the forecast model with only assimilated SIC, the forecast accuracy of SIC, sea ice extent, and SIT in the Arctic melting season is improved and does not weaken with the increase in the forecast time. Especially in the central Arctic, the average absolute deviation between 24-h SIT forecast results and observations is only 0.16 m. The results indicate great potential for applying the reconstructed SIT to the operational forecast of Arctic sea ice during the melting season in the future. Significance Statement To improve the prediction skills of Arctic sea ice, it is necessary to assimilate the sea ice observation into the dynamic model to generate a more realistic initial prediction field. At present, the observation data of daily sea ice thickness (SIT) during the Arctic melting season are few, which cannot well meet the demand of operational SIT forecast. In this study, a bivariate regression model is put forward to construct SIT using the sea ice concentration (SIC) observation. Benefitting from the joint assimilation of the observed SIC and the reconstructed SIT, the forecast accuracy of sea ice variables is greatly improved. The reconstructed SIT is expected to provide an available dataset for further research on the Arctic sea ice forecast.

AUVs Under Ice: A Four-Decade Retrospective on Strategy and Risk Through the Autosub Looking Glass

Abstract Autonomous underwater vehicles (AUVs) have been used in the polar regions for decades. The technical challenges are well understood, and since the mid 1990s, missions of several hundred kilometers under sea ice have been possible. However, there remains a large gap between the endurance achieved in practice and the 1000+ km needed for large-scale sea ice studies; multiple, parallel sections under West Antarctic ice shelves; or sections under the Filchner-Ronne Ice Shelf. This retrospective examines how the U.K. Autosub program's requirements for under ice observations were determined and how those requirements helped build a strategy for a decade of technical progression and scientific achievement. At times progress was faltering, vehicle reliability was inadequate and the approach to risk management was reactive rather than proactive. New analytical tools, some controversial, were developed to assess and manage risk and to drive improvements. While new satellite techniques arguably reduced the need for deployments under sea ice, they remain the only effective solution to fill data-gaps in bathymetry and ocean/ice interactions under ice shelves. The challenge for international collaboration is to accelerate the development of robust and reliable vehicles. The first task remains to survey the basic geometry of the key Antarctic ice shelf cavities. Taking a decadal view, AUV capability should expand to be capable of yearlong deployments under critical ice shelves.

Modeling seasonal ice and its impact on the thermal regime of a shallow boreal lake using the Canadian small Lake model

At high latitudes, lake-atmosphere interactions are disrupted for several months of the year by the presence of an ice cover. By isolating the water column from the atmosphere, ice, typically topped by snow, drastically alters albedo, surface roughness, and heat exchanges relative to the open water period, with major climatic, ecological, and hydrological implications. Lake models used to simulate the appearance and disappearance of the ice cover have rarely been validated with detailed in situ observations of snow and ice. In this study, we investigate the ability of the physically-based 1D Canadian Small Lake Model (CSLM) to simulate the freeze-up, ice-cover growth, and breakup of a small boreal lake. The model, driven offline by local weather observations, is run on Lake Piché, 0.15 km2 and 4 m deep (47.32°N; 71.15°W) from 25 October 2019 to 20 July 2021, and compared to observations of the temperature profile and ice and snow cover properties. Our results show that the CSLM is able to reproduce the total ice thickness (average error of 15 cm) but not the ice type-specific thickness, underestimating clear ice and overestimating snow ice. CSLM manages to reproduce snow depth (errors less than 10 cm). However, it has an average cold bias of 2°C and an underestimation of average snow density of 34 kg m−3. Observed and model freeze-up and break-up dates are very similar, as the model is able to predict the longevity of the ice cover to within 2 weeks. CSLM successfully reproduces seasonal stratification, the mixed layer depth, and surface water temperatures, while it shows discrepancies in simulating bottom waters especially during the open water period.

Preliminary Exploration of Coverage for Moon-Based/HEO Spaceborne Bistatic SAR Earth Observation in Polar Regions

To address the challenge of achieving both temporal consistency and spatial continuity in Earth observation data of polar regions, this paper proposes an innovative concept of Moon-based/Highly Elliptical Orbit (HEO) Spaceborne Bistatic Synthetic Aperture Radar (MH-BiSAR), with transmitters on the Moon and receivers on HEO satellites. By utilizing ephemeris data and an orbit propagator, this study explores MH-BiSAR’s geometric coverage capabilities in polar regions and conducts a preliminary analysis of its characteristics. The findings reveal that MH-BiSAR could provide continuous multi-day revisit observations of polar regions within each sidereal month, presenting a significant advantage for monitoring high-dynamic and large-scale scientific phenomena, such as polar sea ice observations. This innovative observational method offers a new perspective for polar monitoring and is expected to deepen our understanding of polar phenomena.

Physical and Unphysical Causes of Nonstationarity in the Relationship Between Barents‐Kara Sea Ice and the North Atlantic Oscillation

AbstractThe role of internal variability in generating an apparent link between autumn Barents‐Kara sea (BKS) ice and the winter North Atlantic Oscillation (NAO) has been intensely debated. In particular, the robustness and causality of the link has been questioned by showing that BKS‐NAO correlations exhibit nonstationarity in both reanalysis and climate model simulations. We show that the lack of ice observations means nonstationarity cannot be confidently assessed using reanalysis prior to 1961. Model simulations are used to corroborate an argument that forced nonstationarity could result from ice edge changes due to global warming. Consequently, the observed change in BKS‐NAO correlations since 1960 might not be purely a result of internal variability and may also reflect that the ice edge has moved. The change could also reflect the availability of more accurate ice observations. We discuss potential implications for analysis based on coupled climate models, which exhibit large ice edge biases.

JWST observations of 13CO2 ice

The structure and composition of simple ices can be severely modified during stellar evolution by protostellar heating. Key to understanding the involved processes are thermal and chemical tracers that can be used to diagnose the history and environment of the ice. The 15.2 µm bending mode of 12CO2 in particular has proven to be a valuable tracer of ice heating events but suffers from grain shape and size effects. A viable alternative tracer is the weaker 13CO2 isotopologue band at 4.39 µm, which has now become accessible at high S/N with the James Webb Space Telescope (JWST). In this study, we present JWST NIRSpec observations of 13CO2 ice in five deeply embedded Class 0 sources that span a wide range in masses and luminosities (0.2–104 L⊙) taken as part of the Investigating Protostellar Accretion Across the Mass Spectrum (IPA) program. The band profiles vary significantly depending on the source, with the most luminous sources showing a distinct narrow peak at 4.38 µm. We first applied a phenomenological approach with which we demonstrate that a minimum of three to four Gaussian profiles are needed to fit the absorption feature of 13CO2. We then combined these findings with laboratory data and show that a 15.2 µm 12CO2 bending-mode-inspired five-component decomposition can be applied to the isotopologue band, with each component representative of CO2 ice in a specific molecular environment. The final solution consists of cold mixtures of CO2 with CH3OH, H2O, and CO as well as segregated heated pure CO2 ice at 80 K. Our results are in agreement with previous studies of the 12CO2 ice band, further confirming that 13CO2 is a useful alternative tracer of protostellar heating and ice composition. We also propose an alternative solution consisting only of heated mixtures of CO2:CH3OH and CO2:H2O ices and warm pure CO2 ice at 80 K (i.e., no cold CO2 ices) for decomposing the ice profiles of HOPS 370 and IRAS 20126, the two most luminous sources in our sample that show strong evidence of ice heating resulting in ice segregation.

Improving short-term sea ice concentration forecasts using deep learning

Abstract. Reliable short-term sea ice forecasts are needed to support maritime operations in polar regions. While sea ice forecasts produced by physically based models still have limited accuracy, statistical post-processing techniques can be applied to reduce forecast errors. In this study, post-processing methods based on supervised machine learning have been developed for improving the skill of sea ice concentration forecasts from the TOPAZ4 prediction system for lead times from 1 to 10 d. The deep learning models use predictors from TOPAZ4 sea ice forecasts, weather forecasts, and sea ice concentration observations. Predicting the sea ice concentration for the next 10 d takes about 4 min (including data preparation), which is reasonable in an operational context. On average, the forecasts from the deep learning models have a root mean square error 41 % lower than TOPAZ4 forecasts and 29 % lower than forecasts based on persistence of sea ice concentration observations. They also significantly improve the forecasts for the location of the ice edges, with similar improvements as for the root mean square error. Furthermore, the impact of different types of predictors (observations, sea ice, and weather forecasts) on the predictions has been evaluated. Sea ice observations are the most important type of predictors, and the weather forecasts have a much stronger impact on the predictions than sea ice forecasts.

Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiC

AbstractModels struggle to accurately simulate observed sea ice thickness changes, which could be partially due to inadequate representation of thermodynamic processes. We analyzed co‐located winter observations of the Arctic sea ice from the Multidisciplinary Drifting Observatory for the Study of the Arctic Climate for evaluating and improving thermodynamic processes in sea ice models, aiming to enable more accurate predictions of the warming climate system. We model the sea ice and snow heat conduction for observed transects forced by realistic boundary conditions to understand the impact of the non‐resolved meter‐scale snow and sea ice thickness heterogeneity on horizontal heat conduction. Neglecting horizontal processes causes underestimating the conductive heat flux of 10% or more. Furthermore, comparing model results to independent temperature observations reveals a ∼5 K surface temperature overestimation over ice thinner than 1 m, attributed to shortcomings in parameterizing surface turbulent and radiative fluxes rather than the conduction. Assessing the model deficiencies and parameterizing these unresolved processes is required for improved sea ice representation.

A Google Earth Engine Platform to Integrate Multi-Satellite and Citizen Science Data for the Monitoring of River Ice Dynamics

This study introduces a new automated system that blends multi-satellite information and citizen science data for reliable and timely observations of lake and river ice in under-observed northern regions. The system leverages the Google Earth Engine resources to facilitate the analysis and visualization of ice conditions. The adopted approach utilizes a combination of moderate and high-resolution optical data, along with radar observations. The results demonstrate the system’s capability to accurately detect and monitor river ice, particularly during key periods, such as the freeze-up and the breakup. The integration citizen science data showed added values in the validation of remote sensing products, as well as filling gaps whenever satellite observations cannot be collected due to cloud obstruction. Moreover, it was shown that citizen science data can be converted to valuable quantitative information, such as the case of ice thickness, which is very useful when combined with ice extent derived from remote sensing. In this study, citizen science data were employed for the quantitative assessment of the remote sensing product. Obtained results showed a good agreement between the product and observed river status, with a Critical Success Index of 0.82. Notably, the system has shown effectiveness in capturing the spatial and temporal evolution of snow and ice conditions, as evidenced by its application in analyzing specific ice jam events in 2023. The study concludes that the developed system marks a significant advancement in river ice monitoring, combining technological innovation with community engagement.

Multisensor data fusion of operational sea ice observations

Multisensor data fusion (MDF) is a process/technique of combining observations from multiple sensors to provide a more robust, accurate and complete description of the concerned object, environment or process. In this paper we introduce a new MDF method, multisensor optimal data fusion (MODF), to fuse different operational sea ice observations around Svalbard. The overall MODF includes regridding, univariate multisensor optimal data merging (MODM), multivariate check of consistency, and generation of new variables. For MODF of operational sea ice observations around Svalbard, the AMSR2 sea ice concentration (SIC) is firstly merged with the Norwegian Meteorological Institute ice chart. Then the daily SMOS sea ice thickness (SIT) is merged with the weekly CS2SMOS SIT to form a daily CS2SMOS SIT, which is further refined to be consistent with the SIC through consistency check. Finally sea ice volume (SIV) and its uncertainty are calculated based on the merged SIC and fused SIT. The fused products provide an improved, united, consistent and multifaceted description for the operational sea ice observations, they also provide consistent descriptions of sea ice edge and marginal ice zone. We note that uncertainties may vary during the regridding process, and therefore correct determination of the observation uncertainties is critically important for MDF. This study provides a basic framework for managing multivariate multisensor observations.

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.

Amorphous 1-propanol interstellar ice beyond its melting point

ABSTRACT The recent discovery of 1-propanol (CH3CH2CH2OH) in the interstellar medium (ISM) is of tremendous interest since fatty alcohols have been proposed as constituents of proto-cell membranes. Motivated by this discovery, we present the laboratory mid-infrared (MIR) and vacuum ultraviolet (VUV) absorption spectra of 1-propanol ice under astrochemical conditions, mimicking an icy mantle on cold dust in the ISM. Both MIR and VUV spectra were recorded at ultrahigh vacuum of ∼10-9 mbar and at temperatures ranging from 10 K to sublimation. The morphology of the 1-propanol ice deposited at 10 K was amorphous. By warming the ice to temperatures of 140 K and above, with subsequent recording of IR spectra, we observe complete sublimation of 1-propanol molecules from the substrate around 170 K. No amorphous-to-crystalline phase change was observed upon warming to higher temperatures. Additionally, we observe the IR and VUV signatures of 1-propanol ice on the substrate well beyond its melting point (147 K). To the best of our knowledge, this is the first reported observation of a molecular ice staying well beyond its melting point under such conditions. This result shows that the morphology of icy mantles on ISM cold dust grains is more complex than previously thought. Our atomistic molecular dynamics simulations capture the experimental trends and shed light on the microscopic origin of this unusual phase behaviour of 1-propanol.

The range of ice thrusts and ice piles as reflected by ice scars on trees growing on the shores of coastal lagoons: The case of the Szczecin Lagoon

We studied the phenomena associated with the thrusting of ice onto the shore of the Szczecin Lagoon based on the occurrence of tree ice scars. The measurements concerned mostly the maximum height, length and width of ice scars on trees, and the distance of these trees from the shore in the period 2017–2022. It was observed that sheets of ice advanced up to 64 m inland, and piled to form hummocks reaching up to 4.3 m above the water level. These maximum values occurred mostly on eastern shores, which is where the highest numbers of damaged and broken trees were observed. This should be associated with the strongest and most frequently occurring wind blowing from the western direction in the winter-spring period. To the contrary, the lowest number of damaged trees were observed on the western shore. This is due not only to the lower frequency of wind blowing from the east, and the associated cooling (Ta < 0°C) and ice cover stabilization, but also due to the presence of extensive reed belts. Our results enable an indirect insight into the ice phenomena dynamics, especially in areas lacking systematic ice observations. Similar conclusions may be extended for all the sheltered basins as lakes or lagoons.