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There are few studies where the evolution of grain orientations and crystal misorientations of neighboring grains in a polycrystalline ice sample during grain growth are analyzed. In this work, crystal orientations and misorientations were studied in two ice cylinders obtained from bidistilled water using the techniques of plastic replicas and chemical etching pits. The cylinders were grown, one through fast freezing and the other through slow freezing. It was observed that the initial crystal orientations of the grains varied from one cylinder to the other, but this did not result in significant differences in the final crystal misorientations between neighboring grains. Such crystal misorientations could be special because they are related to a coincidence sites lattice (CSL), which could be associated with low-energy grain boundaries. Therefore, the decrease in grain boundary energy during grain growth could be influenced by these special crystal misorientations. [ABSTRACT FROM AUTHOR]

Using the psychometric paradigm of risk in conjunction with surveys of the Michigan public (n = 638) and a regional planning organization (n = 65), we examine the perceived risk and concerns associated with underwater oil pipelines, the Enbridge Line 5 pipeline in particular, and oil spills under ice. The fate of Line 5 is heavily debated in Michigan, specifically the portion that traverses the Straits of Mackinac, which can be ice-covered for months. Scant literature examines how individuals perceive the risk associated with Line 5, its alternatives, or potential spills in open water or under ice. Here we identify considerable concern regarding both the pipeline and the potential for spills under ice on behalf of the public, and increased concern about spills under ice on behalf of the planning organization. Organization members' concerns are significantly predicted by beliefs about the difficulty in remediating spills, however not by beliefs about spills' likelihood, difficulty in detection, noticeability, or consequences. Our results identify the need to better examine and communicate the risks associated with underwater pipelines and spills, both in open water and under ice, as well as options for remediating oil captured under ice. Furthermore, we recommend the adoption of decision-making and risk governance processes that explicitly expand analysis of the social, economic and environmental tradeoffs of underwater pipelines such as Line 5. [ABSTRACT FROM AUTHOR]

High-resolution lake ice/water observations retrieved from satellite imagery through efficient, automated methods can provide critical information to lake ice forecasting systems. Synthetic aperture radar (SAR) data is well-suited to this purpose due to its high spatial resolution (approximately 50 m). With recent increases in the volume of SAR data available, the development of automated retrieval methods for these data is a priority for operational centres. However, automated retrieval of ice/water data from SAR imagery is difficult, due to ambiguity in ice and open water signatures, both in terms of image tone and in terms of parameterized texture features extracted from these images. Convolutional neural networks (CNNs) can learn features from imagery in an automated manner, and have been found effective in previous studies on sea ice concentration estimation from SAR. In this study the use of CNNs to retrieve ice/water observations from dual-polarized SAR imagery of two of the Laurentian Great Lakes, Lake Erie and Lake Ontario, is investigated. For data assimilation, it is crucial that the retrieved observations are of high quality. To this end, quality control measures based on the uncertainty of the CNN output to eliminate incorrect retrievals are discussed and demonstrated. The quality control measures are found to be effective in both dual-polarized and single-polarized retrievals. The ability of the CNN to downscale the coarse resolution training labels is demonstrated qualitatively. [ABSTRACT FROM AUTHOR]

Lake Ontario ice conditions are statistically linked to regional temperatures recorded in Toronto, during the most recent climate normal (1980/81–2009/10). A metric was developed to capture the net melting effect of average winter temperatures to characterize lake ice conditions, referred to as Net Melting-Degree Days (NMDD). This metric was able to account for 78% of lake ice interannual variability (R2 = 0.783, P < 0.001). Based on NMDD parameters, current lake ice conditions were characterized in four ways: heavy, moderate, light and very light. Lake Ontario ice conditions were reconstructed to create a hindcast for the span of the instrumental temperature record (1840/41–1979/80). Based on a decadal analysis, heavy ice seasons decreased significantly (R2 = 0.658, P < 0.001) from the 1840s to the 2000s, declining from an average of 6 heavy ice seasons per decade during the most distant climate normal (1840s to 1960s) to an average of only 1 heavy ice season per decade during the most recent climate normal (1980s to 2000s). Finally, lake ice conditions are projected to the end of the 21st century, using an optimal ensemble of Global Climate Model outputs for two different climate change scenarios (RCP4.5, RCP8.5). Heavy ice seasons no longer occur as early as the 2050s under both RCP4.5 and RCP8.5. Whereas, very light ice seasons go from being an extreme in the baseline period (10%), to the dominant characterization of Lake Ontario ice conditions by the 2080s, for both RCP4.5 (73%) and RCP8.5 (100%). [ABSTRACT FROM AUTHOR]

The aim of the study was to evaluate, for the first time, the capability of different machine learning (ML) algorithms in classifying along-track lake surface conditions (open water and ice types) across ice seasons (freeze-up, ice growth and break-up periods) from Sentinel-3 A/B synthetic aperture radar altimeter (SRAL) data. To achieve this goal, over 107,500 radar waveforms extracted from 11 large lakes across the Northern Hemisphere and three ice seasons (2018–2021) were manually labelled using complementary satellite data (Sentinel-1 imaging Synthetic Aperture Radar (SAR), Sentinel-2 Multispectral Instrument (MSI) Level 1C, and MODIS Aqua/Terra data) for the training and testing of the ML algorithms in discriminating between open water, young (thin) ice, growing ice and melting ice. The four ML algorithms tested include Random Forest (RF), Gradient Boosting Trees (GBT), K Nearest Neighbor (KNN) and Support Vector Machine (SVM). To characterize the waveforms, seven waveform parameters were derived: Leading Edge Width (LEW), Offset Center of Gravity (OCOG) Width, Pulse Peakiness (PP), backscatter coefficient (Sigma0), late tail to peak power (LTPP), early tail to peak power (ETPP) and the maximum value of the echo power (Max). Accuracies >95% were achieved across all classifiers using a 4-parameter combination (Sigma0, PP, OCOG Width, and LEW). Among all waveform parameters, Sigma0, OCOG width and PP were found to be the most important parameters for discriminating between lake ice types and open water. Despite showing comparable classification performances in the overall classification, RF and KNN are found to be a better fit for global lake ice mapping as both are less sensitive to their internal hyperparameters. Additionally, consistent results (>93.7% accuracy in all classifiers) achieved on the accuracy assessment carried out for each lake (out-of-sample testing) revealed the strength of the classifiers for spatial transferability. Implementation of RF and KNN could be valuable in a pre-or post-processing step for identifying lake surface conditions under which the retrieval of water level and ice thickness may be limited or not possible and, therefore, inform algorithms currently used for the generation of operational or research products. While the research focused on 11 of the largest lakes of the Northern Hemisphere, the classification approach presented herein has potential for application on smaller lakes too since data in SAR mode (∼300 m along-track resolution) are used. • This is the first study using Sentinel-3 SAR altimetry for lake ice classification. • Four different machine learning classifiers (SVM, KNN, RF and GBT) were assessed. • RF and KNN are recommended for classification of ice types along altimeter tracks. • Sigma0, OCOG Width, PP are the best parameters to differentiate lake ice and water. • Over 93.7% accuracy in spatial cross-validation shows broad-scale applicability. [ABSTRACT FROM AUTHOR]

Late-winter lake ice regimes are controlled by water depth relative to maximum ice thickness (MIT). When MIT exceeds maximum water depth, lakes freeze to the bottom with bedfast ice (BI) and when MIT is less than maximum water depth lakes have floating ice (FI). Both airborne radar and space-borne synthetic aperture radar (SAR) imagery (Ku-, X-, C-, and L-band) have been used previously to determine whether lakes have a BI or FI regime in a given year, across a number of years, or across large regions. In this study, we use a combination of ERS-1/2, RADARSAT-2, Envisat, and Sentinel-1 SAR imagery for seven lake-rich regions in Arctic Alaska to analyze lake ice regime extents and dynamics over a 25-year period (1992–2016). Our interactive threshold classification method determines a unique statistic-based intensity threshold for each SAR scene, allowing for the comparison of classification results from C-band SAR data acquired with different polarizations and incidence angles. Additionally, our novel method accommodates declining signal strength in aging extended-mission satellite SAR instruments. Comparison of SAR ice regime classifications with extensive field measurements from six years yielded a 93% accuracy. Significant declines in BI regimes were only observed in the Fish Creek area with 3% of lakes exhibiting transitional ice regimes—lakes that switch from BI to FI during this 25-year period. This analysis suggests that the potential conversion from BI to FI regimes is primarily a function of lake depth distributions in addition to regional differences in climate variability. Remote sensing of lake ice regimes with C-band SAR is a useful tool to monitor the associated thermal impacts on permafrost, since lake ice regimes can be used as a proxy for of sub-lake permafrost thaw, considered by the Global Climate Observing System as an Essential Climate Variable (ECV). Continued winter warming and variable snow conditions in the Arctic are expected and our long-term analysis provides a valuable baseline for predicting where potential future lake ice regimes shifts will be most pronounced. [ABSTRACT FROM AUTHOR]

Lake ice is a sensitive indicator of climate change. Based on the disparities between the brightness temperatures of lake ice and water, passive microwave data can be used to monitor the ice variations of a lake. With focus on the analysis of long time series variability of lake ice, this study extracts four characteristic dates related to lake ice (the annual freeze start, freeze completion, ablation start and ablation completion dates) for Qinghai Lake from 1979 to 2016 using Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I) passive microwave brightness temperature data. The corresponding freezing duration, ablation duration, complete freezing duration and ice coverage duration are calculated. Applying Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products, the accuracy of the results derived from passive microwave data is validated. The validation analysis shows a strong agreement (R 2 ranges from 0.70 to 0.85, mean absolute error (MAE) ranges from 2.25 to 3.94 days) in the freeze start, ablation start, and ablation completion dates derived from the MODIS data and passive microwave data; the ice coverage duration also has a small error (relative error (RE) = 2.95%, MAE = 3.13 days), suggesting that the results obtained from passive microwave data are reliable. The results show that the freezing dates of Qinghai Lake have been delayed and the ablation dates have advanced. Over 38 years, the freeze start date and freeze completion date have been pushed back by 6.16 days and 2.27 days, respectively, while the ablation start date and ablation completion date have advanced by 11.24 days and 14.09 days, respectively. The freezing duration and ablation duration have shortened by 3.89 days and 2.85 days, respectively, and the complete freezing duration and ice coverage duration have shortened by 14.84 days and 21.21 days, respectively. There is a significant negative correlation between the ice coverage duration and the mean air temperature in winter. [ABSTRACT FROM AUTHOR]

The seasonal dynamics of freshwater lake ice and its interactions with air and snow are studied in two small subarctic lakes with comparable surface areas but contrasting depths (4.3 versus 91 m). Two, 2.9 m long thermistor chain sensors (Snow and Ice Mass Balance Apparatuses), were used to remotely measure air, snow, ice, and water temperatures every 15-min between December 2021 and March 2022. Results showed that freeze-up occurred later in the deeper lake (Ryan Lake) and earlier in the shallow lake (Landing Lake). Ice growth was significantly faster in Ryan Lake than in Landing Lake, due to cold water temperatures (mean (T w ¯) =0.65 to 0.96°C) persisting beneath the ice. In Landing Lake, basal ice growth was hindered because of warm water temperatures (T w ¯ =1.5 to 2.1°C) caused by heat released from lake sediments. Variability in air temperatures at both lakes had significant influences on the thermal regimes of ice and snow, particularly in Ryan Lake, where ice temperatures were more sensitive to rapid changes in air temperatures. This finding suggests that conductive heat transfer through the air-water continuum may be more sensitive to variability in air temperatures in deeper lakes with colder water temperatures than in shallow lakes with warmer water temperatures, if snow depths and densities are comparable. This study highlights the significance of lake morphology and rapid air temperature variability on influencing ice growth processes. Conclusions drawn aim to improve the representation of ice growth processes in regional and global climate models, and to improve ice safety for northern communities. • Two autonomous ice sensors were deployed in two Canadian subarctic lakes. • Air, snow, ice, and water temperatures measured at 15-min intervals for 4 months. • Ice temperatures were more sensitive to variable air temperatures in the deep lake. • Ice growth in shallow lake was inhibited by heat released from lake sediments. • Small lakes may act as significant regional sources of conductive heat. [ABSTRACT FROM AUTHOR]

Lake ice provides important social and economic services to local communities, in addition to being a sensitive indicator of climate change. The reduction of ground observations of freshwater ice has led to an increased reliance on the use of satellite remote sensing data. There is currently interest in the retrieval of lake ice properties (e.g., ice thickness, bubble radius, roughness) using synthetic aperture radar (SAR). Roughness at the ice-water interface is particularly important as it has been identified as the dominant mechanism for increasing SAR backscatter throughout the ice season and must be considered in numerical radiative transfer models. Therefore, this study determines optimal ice-water interface roughness height for two subarctic lakes in northern Canada and models backscatter throughout two ice seasons using the snow microwave radiative transfer (SMRT) model. The two lakes for this study are Noell Lake and Malcolm Ramsay Lake. Field observations of ice thickness, snow depth, snow density, and the Canadian Lake Ice Model (CLIMo) are used to parameterize SMRT. Modelled L, C, and X-band backscatter at different incidence angles is assessed using SAR imagery from multiple satellite missions. Root mean square errors ranged from 0.38 to 1.45 dB for Noell Lake and 0.70 to 2.33 dB for Malcolm Ramsay Lake. Discrepancies between modelled and observed backscatter were found to be connected to the representation of roughness at different interfaces within the ice column and changes that occurred during freeze-melt events. These results provide insight into how changes in ice properties impact backscatter throughout the ice season. SMRT is valuable for modelling backscatter from lake ice during the cold season and could be used to develop retrieval algorithms for estimating ice-water interface roughness. This would allow for the development of other inversion models for retrieval of surface ice conditions and ice thickness. • First application of SMRT for modelling backscatter over a complete ice season. • Multi-frequency backscatter was successfully modelled for two Subarctic lakes. • Variable root mean square height was an improvement over a constant value. • Changes in the ice surface and snowpack result in discrepancies in modelling. • SMRT can be useful for future retrieval of crucial lake ice properties. [ABSTRACT FROM AUTHOR]

Several lake ice phenology studies from satellite data have been undertaken. However, the availability of long-term lake freeze-thaw-cycles, required to understand this proxy for climate variability and change, is scarce for European lakes. Long time series from space observations are limited to few satellite sensors. Data of the Advanced Very High Resolution Radiometer (AVHRR) are used in account of their unique potential as they offer each day global coverage from the early 1980s expectedly until 2022. An automatic two-step extraction was developed, which makes use of near-infrared reflectance values and thermal infrared derived lake surface water temperatures to extract lake ice phenology dates. In contrast to other studies utilizing thermal infrared, the thresholds are derived from the data itself, making it unnecessary to define arbitrary or lake specific thresholds. Two lakes in the Baltic region and a steppe lake on the Austrian–Hungarian border were selected. The later one was used to test the applicability of the approach to another climatic region for the time period 1990 to 2012. A comparison of the extracted event dates with in situ data provided good agreements of about 10 d mean absolute error. The two-step extraction was found to be applicable for European lakes in different climate regions and could fill existing data gaps in future applications. The extension of the time series to the full AVHRR record length (early 1980 until today) with adequate length for trend estimations would be of interest to assess climate variability and change. Furthermore, the two-step extraction itself is not sensor-specific and could be applied to other sensors with equivalent near- and thermal infrared spectral bands. [ABSTRACT FROM AUTHOR]

Time series of ground-based X- and Ku-band frequency-modulated continuous-wave (FMCW) radar data are used to derive ice thickness for bubbled freshwater lake ice with heterogeneous snow cover under the assumption of interactions in range occurring at the ice–snow and ice–water interface. Once adjusted for the refractive index of ice and slant range, the distance between peak returns agrees with in-situ ice thickness observations. Ice thicknesses are derived from the distance of peak returns in range acquired in the off-nadir incidence angle range 21°–60°. Derived ice thicknesses are compared to in-situ measurements, an upward-looking acoustic sonar (Shallow Water Ice-Profiler (SWIP)), and a one-dimensional thermodynamic lake ice model (Canadian Lake Ice Model (CLIMo)). Median ice thicknesses derived with University of Waterloo X- and Ku-band scatterometers (UW-Scat) agreed well with in-situ measurements ( R 2 = 0.953 and 0.964), SWIP ( R 2 = 0.842 and 0.893), and Canadian Lake Ice Model (CLIMo) simulations using 25% of terrestrial snowpack scenario, respectively. UW-Scat derived ice thicknesses produced root mean square error (RMSE) values of 0.053 and 0.088 m for X- and Ku-band, respectively, relative to in-situ ice thickness measurements. This study is the first FMCW X- and Ku-band off-nadir approach to observe interactions at the snow–ice and ice–water interface to derive ice thickness. [ABSTRACT FROM AUTHOR]

Observations of lake ice at the shore, complete ice cover, ice duration, ice thickness and other measures for 18 Polish lakes were collected for the 50 year period (1961–2010). Average ice dates in early winter became later: first appearance of ice along shore 2.3 days decade −1 and complete ice cover 1.2 days decade −1 while complete ice cover disappeared earlier (5.6 days decade −1 ) as did last ice at the shore (4.3 days decade −1 ). The duration of ice cover decreased by 5.6 days decade −1 and average ice thickness declined by 6.1 cm decade −1 . The magnitude of these values for individual lakes decreased from eastern to western Poland. This geographic gradient is likely related to regional atmospheric circulation because in winter this part of Europe is strongly affected by continental air, an influence that is greater in the east. A multivariate redundancy analysis (RDA), used in order to examine the dependence of ice measures on lake physical properties and location, indicated longitude and altitude as key factors explaining lake ice dynamics such as the disappearance of ice and ice cover, ice cover duration and thickness. Lake volume and average depth influenced mostly the appearance of ice and ice cover. [ABSTRACT FROM AUTHOR]

This paper concerns the response of the conditioned by orography cryosphere of the non-glaciated mountains of mid-latitude to the climate impulses. It presents the relationships among the air temperature, precipitation, snow cover, lake ice cover, firn-ice patches (glacierets) and permafrost in the Tatras. The data from the warmest multi-year in the history of the local meteorological measurements and statistical models (multiple regression) have been used. The results indicate that all the components of the contemporary cryosphere are very sensitive to the changes in the air temperature in winter or snow precipitation/accumulation. Due to the diverse orographic conditions, interannual variability of seasonal and perennial, surface and subsurface ice deposits in the mountain areas may not be synchronous. However, the long-term trends of this variability reflect the changes in the global climate system. [ABSTRACT FROM AUTHOR]

Abstract: A hydrodynamic model that includes ice processes and is optimized for parallel processing was configured for Lake Erie in order to study the ice–water coupling processes in the lake. A hindcast from April 2003 to December 2004 with hourly atmospheric forcing was conducted. The model reproduced the seasonal variation of ice cover, but the development of ice extent in January and its decay in March somewhat preceded the observations. Modeled lake circulation in ice-free seasons is consistent with previous studies for Lake Erie. Thermal structure of the lake was reasonably comparable to both satellite-derived observations and in-situ measurements, with mean differences ranging from −2°C to 4°C, depending on the season. The impacts of ice–water stress coupling and basal melting of ice were examined based on numerical experiments. The results show that: 1) ice–water stress coupling significantly dampens the subjacent lake circulation in winter due to packed ice cover that slows down the surface water, and 2) basal melting of ice contributes to widespread ice cover in the lake. The demonstrated model validity could lead to further studies of ice–water processes in the lake, including interannual variation and impacts on ecosystems. [Copyright &y& Elsevier]

Abstract: The spatial resolution of passive microwave observations from space is of the order of tens of kilometers with currently available instruments, such as the Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer (AMSR-E). The large field of view of these instruments dictates that the observed brightness temperature can originate from heterogeneous land cover, with different vegetation and surface properties. In this study, we assess the influence of freshwater lakes on the observed brightness temperature of AMSR-E in winter conditions. The study focuses on the geographic region of Finland, where lakes account for 10% of the total terrestrial area. We present a method to mitigate for the influence of lakes through forward modeling of snow covered lakes, as a part of a microwave emission simulation scheme of space-borne observations. We apply a forward model to predict brightness temperatures of snow covered sceneries over several winter seasons, using available data on snow cover, vegetation and lake ice cover to set the forward model input parameters. Comparison of model estimates with space-borne observations shows that the modeling accuracy improves in the majority of examined cases when lakes are accounted for, with respect to the case where lakes are not included in the simulation. Moreover, we present a method for applying the correction to the retrieval of Snow Water Equivalent (SWE) in lake-rich areas, using a numerical inversion method of the forward model. In a comparison to available independent validation data on SWE, also the retrieval accuracy is seen to improve when applying the influence of snow covered lakes in the emission model. [Copyright &y& Elsevier]

Abstract: There is probably no more important characteristic of river and lake ice than the thickness. This paper consists of three parts: In Part I the thickening of ice via conduction is analyzed and tested against an extensive data set assembled by Bilello (1961–1996) that included weekly measurements of ice thickness and snow on the ice for a number of sites in Alaska and Canada. It is shown that the largest variations from year to year at a given site are associated with the thickness of the snow on the ice, and secondarily by variations in the coldness of the winter period of thickening. If no snow ice forms, quite good agreement with a simple thickening algorithm is achieved. This leads to Part II where the formation of snow ice is analyzed but the analysis is constrained by the fact that sometimes, even though the weight of the snow is enough to submerge the solid ice cover, there seems to be no snow ice formation. Differences between the formation of snow ice on river and lake ice covers are examined. For river ice covers, the water from below cannot always find a path to the top of the ice cover. Finally, in Part III the thinning of river ice covers is analyzed using a simple algorithm based on solar radiation calculations and sensible and latent heat transfers from the air to the ice. Good agreement is obtained with the extensive data set of Bilello (1980). Implications for practical calculations of thickness are discussed. [Copyright &y& Elsevier]

Abstract: Formation of lake ice is common in lakes located in mid and high latitudes. Lake ice plays a vital role in heat storage, controlling lake water temperature, survival of aquatic ecosystems, and maintaining the bio diversity of lakes. Significant warming in air temperature during the cold season (October–May) may lead to reduced ice cover of lakes and eventually disturb the lake''s seasonal dynamics. We examined the role of climate variability on lake ice phenology for small inland lakes in the Great Lakes region. The Variable Infiltration Capacity (VIC) model with a physically based lake algorithm was implemented to simulate long term (i.e. 1916–2007) changes in lake ice phenology, as described by the date of ice break-up, date of ice freeze-up, and number of ice-free days. Model performance was evaluated against observed lake ice phenology. A statistically significant increase (0.08–0.21°C) in air temperature resulted in a significant change (0.2–2.0days/decade) in lake ice freeze-up and break-up dates. While lake ice freeze-up was strongly associated (correlation>0.60) with air temperature of the early (October–December) cold season, lake ice break-up was highly associated (correlation=−0.80) with air temperature during the late (March–May) cold season. The number of ice-free days was affected by the temperature changes during the entire cold season. Lakes located in the southern part of the study domain experienced stronger trends in ice phenology than those located in the northern part. Inter-decadal to decadal scale variability in the number of ice-free days not associated with long-term trends in air temperature were largely driven by the Atlantic Multi-decadal Oscillation (AMO), El-Niño Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO). [Copyright &y& Elsevier]

Abstract: The phenomenon of “temperature dichotomy”, or anomalous heating of surface water under the ice up to temperatures exceeding 4°C is known to take place occasionally in solar-heated ice-covered freshwater lakes and has usually been explained by the stabilizing effect of the weak vertical salinity gradient created by the melt water flux from the ice and supporting the unstable temperature distribution. Here, we report an observation of the local temperature maximum in the upper part of the water column of ice-covered Lake Vendyurskoe (northwestern Russia). The observation was accompanied by vertically resolved measurements of the conductivity allowing estimation of the dissolved salts effect on the vertical density distribution. The results demonstrate insufficiency of the salt gradient to support the vertical stability of water column. Therefore, we suggest the vertically inhomogeneous radiation absorption to be the probable stabilizing mechanism here, similarly to radiatively heated boundary layers in the ocean, the atmosphere and star interiors. An analytical solution of the heat transfer equation is derived describing the temperature profile evolution in ice-covered lakes subject to solar radiation heating above the maximum density temperature. Observed daytime temperature profiles agree well with the analytical model that suggests the absence of convective mixing. According to the model, the temperature maximum is formed within a day that supports the hypothesis about the stabilizing effect of the solar radiation absorption. We conclude that in temperate lakes the warm layer should have diurnal character and should be destroyed during the nighttime by convection. In polar lakes, in turn, the warm layer can exist during essentially longer periods that is supported by application of the model to the previously published data from Lake Peters, Alaska. In this case, the ice melting rate can be significantly affected by the increased temperature gradient beneath the ice. Apart from potential effects on the ice melting rate and the spring plankton development in lakes, the regime represents a rare geophysical example of instability driven solely by radiative heating with many useful analogies in planetary and stellar physics. [Copyright &y& Elsevier]

Abstract: The algorithms designed to estimate snow water equivalent (SWE) using passive microwave measurements falter in lake-rich high-latitude environments due to the emission properties of ice covered lakes on low frequency measurements. Microwave emission models have been used to simulate brightness temperatures (Tbs) for snowpack characteristics in terrestrial environments but cannot be applied to snow on lakes because of the differing subsurface emissivities and scattering matrices present in ice. This paper examines the performance of a modified version of the Helsinki University of Technology (HUT) snow emission model that incorporates microwave emission from lake ice and sub-ice water. Inputs to the HUT model include measurements collected over brackish and freshwater lakes north of Inuvik, Northwest Territories, Canada in April 2008, consisting of snowpack (depth, density, and snow water equivalent) and lake ice (thickness and ice type). Coincident airborne radiometer measurements at a resolution of 80×100m were used as ground-truth to evaluate the simulations. The results indicate that subsurface media are simulated best when utilizing a modeled effective grain size and a 1mm RMS surface roughness at the ice/water interface compared to using measured grain size and a flat Fresnel reflective surface as input. Simulations at 37GHz (vertical polarization) produce the best results compared to airborne Tbs, with a Root Mean Square Error (RMSE) of 6.2K and 7.9K, as well as Mean Bias Errors (MBEs) of −8.4K and −8.8K for brackish and freshwater sites respectively. Freshwater simulations at 6.9 and 19GHz H exhibited low RMSE (10.53 and 6.15K respectively) and MBE (−5.37 and 8.36K respectively) but did not accurately simulate Tb variability (R=−0.15 and 0.01 respectively). Over brackish water, 6.9GHz simulations had poor agreement with airborne Tbs, while 19GHz V exhibited a low RMSE (6.15K), MBE (−4.52K) and improved relative agreement to airborne measurements (R=0.47). Salinity considerations reduced 6.9GHz errors substantially, with a drop in RMSE from 51.48K and 57.18K for H and V polarizations respectively, to 26.2K and 31.6K, although Tb variability was not well simulated. With best results at 37GHz, HUT simulations exhibit the potential to track Tb evolution, and therefore SWE through the winter season. [Copyright &y& Elsevier]

Summary: Ice duration has shortened and the ice-off date has become earlier for Lake Mendota from 1905 to 2000 as air temperatures have warmed and snowfall has increased. In addition, the ice record has cyclic components at inter-annual and inter-decadal scales. We examined the frequency domain relations between ice, local climate and the teleconnections, Southern Ocean Oscillation (SOI), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and Northern Pacific Index (NP), through a three-tiered analysis of coherence. The coherence results provide evidence of linear relations between the three levels at inter-annual and inter-decadal frequencies. Of the three local climate variables analyzed, namely temperature, snowfall and snow depth, temperature is the variable that most significantly affects ice duration and ice-off date, at both inter-annual and inter-decadal frequencies. The most significant effect of teleconnections on local climate are the effects of PDO on snowfall and snow depth, and SOI on temperature, at inter-annual frequencies, and the effect of NAO on snowfall at inter-decadal frequencies. The teleconnections that most significantly affect ice-cover duration and ice-off date, particularly at inter-decadal frequencies, are the PDO and the NAO. The influence of PDO on ice-cover appears to be transmitted through temperature, while the influence of the NAO appears to be transmitted through temperature and snowfall. A cascading set of links between teleconnections, local climate, and lake ice explain some, but not all, of the dynamics in these time series. [Copyright &y& Elsevier]

Impacts of global climate change are expected to result in greater variation in the seasonality of snowpack, lake ice, and vegetation dynamics in southwest Alaska. All have wide-reaching physical and biological ecosystem effects in the region. We used Moderate Resolution Imaging Spectroradiometer (MODIS) calibrated radiance, snow cover extent, and vegetation index products for interpreting interannual variation in the duration and extent of snowpack, lake ice, and vegetation dynamics for southwest Alaska. The approach integrates multiple seasonal metrics across large ecological regions. Throughout the observation period (2001¿2007), snow cover duration was stable within ecoregions, with variable start and end dates. The start of the lake ice season lagged the snow season by 2 to 3 months. Within a given lake, freeze-up dates varied in timing and duration, while break-up dates were more consistent. Vegetation phenology varied less than snow and ice metrics, with start-of-season dates comparatively consistent across years. The start of growing season and snow melt were related to one another as they are both temperature dependent. Higher than average temperatures during the El Niño winter of 2002¿2003 were expressed in anomalous ice and snow season patterns. We are developing a consistent, MODIS-based dataset that will be used to monitor temporal trends of each of these seasonal metrics and to map areas of change for the study area. [Copyright &y& Elsevier]

The temporal evolution of the backscatter coefficient, sigma-nought (σ°) from QuikSCAT was evaluated for monitoring ice phenology on Great Bear Lake (66°N, 121°W) and Great Slave Lake (61°40′N, 114°W), Northwest Territories, Canada. Results indicated that σ° from QuikSCAT can be used to detect melt onset, water clear of ice and freeze onset dates on both lakes. An ice phenology algorithm was then developed to assess the spatiotemporal variability on both lakes from QuikSCAT for the period 2000–2006. Results showed that for Great Slave Lake, the average melt onset date occurred on year day (YD) 123, the average water clear of ice date was on YD164, and the average freeze onset date was on YD330. On Great Bear Lake, the average melt onset date occurred on YD139, the average water clear of ice date was YD191, and the average freeze onset date was YD321. Ice cover remained present for at least five weeks longer on Great Bear Lake than on Great Slave Lake and most of the difference can be explained by earlier ice melt on Great Slave Lake. Spatially, on Great Bear Lake, melt onset took place first in the eastern arm, water clear of ice occurred first in southeastern and western arms, and freeze onset appeared first in the northern arm and along the shorelines. On Great Slave Lake, melt onset began first in the central basin and then progressed to the northern and eastern arms later in the season. The central basin of Great Slave Lake cleared earlier than the periphery due to the discharge from the Slave River. Freeze onset on Great Slave Lake occurred first within the east arm, closely followed by the north and west arms, and then finally in the centre of the main basin. [Copyright &y& Elsevier]

We demonstrate the potential of combining satellite altimetry and radiometry for lake ice studies using the example of Lake Baikal in Siberia. We show the synergy using active and passive microwave observations available from the recent satellite altimetry missions (TOPEX/Poseidon, Jason-1, ENVISAT and Geosat Follow-On), complemented by the SSM/I passive data. We assess the applicability of altimetric and radiometric data for ice/water discrimination, and discuss the drawbacks and benefits of each type of sensor. An ice discrimination method, based on the combined use of the data from the four altimetric missions and SSM/I, is proposed and validated using available in situ observations and MODIS imagery. The method is applied to the entire satellite data set and used to define specific dates of ice events (first appearance of ice, formation of stable ice cover, first appearance of open water, complete disappearance of ice) and associated uncertainties. Using these satellite-derived estimates, we can extend the existing time series of ice events in the Southern Baikal up to 2004 and provide new information on the Middle and Northern Baikal, regions where no recent in situ ice cover observations are available. Our data show that over the last 10–15 years, trends towards earlier ice formation and later ice break-up result in a tendency for longer fast ice duration over the whole Lake Baikal. The methods proposed here have been tested for Lake Baikal, but they are applicable for other lakes and water bodies with seasonal ice cover. [Copyright &y& Elsevier]

Abstract: Variability and trends in lake ice dynamics (i. E. Lake ice phenology) are related to climate conditions. Climate influences the timing of lake ice melt and freeze onset, ice duration, and lake thermal dynamics that feedback to the climate system initiating further change. Phenology records acquired in a consistent manner and over long time periods are required to better understand variability and change in climate conditions and how changes impact lake processes. In this study, we present a new technique for extracting lake ice phenology events from historical satellite records acquired by the series of Advanced Very High Resolution Radiometer (AVHRR) sensors. The technique was used to extend existing in-situ measurements for 36 Canadian lakes and to develop records for 6 lakes in Canada''s far north. Comparison of phenology events obtained from the AVHRR record and in-situ measurements show strong agreement (20 lakes, 180 cases) suggesting, with high confidence especially in the case of break-up dates, the use of these data as a complement to ground observations. Trend analysis performed using the combined in-situ and AVHRR record ∼ 1950–2004 shows earlier break-up (average — 0. 18 days/year) and later freeze-up (average 0. 12 days/year) for the majority of lakes analyzed. Less confidence is given to freeze-up date results due to lower sun elevation during this period making extraction more difficult. Trends for the 20 year record in the far north showed earlier break-up (average 0. 99 days/year) and later freeze-up (average 0. 76 days/year). The established lake ice phenology database from the historical AVHRR image archive for the period from 1985 to 2004 will to a certain degree fill data gaps in the Canadian in-situ observation network. Furthermore, the presented extraction procedure is not sensor specific and will enable continual data update using all available satellite data provided from sensors such as NOAA/AVHRR, MetOp/AVHRR, MODIS, MERIS and SPOT/VGT. [Copyright &y& Elsevier]

Lake ice is an important feature of the physical landscape at northern latitudes. Not only does the presence of lake ice help modulate weather and climate but it also plays an important role in travel between northern communities. Furthermore, the thickness of ice cover can impact overwintering fish habitat and access to fresh water during winter months. Lake ice cover and lake ice thickness are two notable climate proxies identified by the Global Climate Observing System (GCOS) as Essential Climate Variables (ECVs). Changes in ice thickness can impact lakes freezing to bed which in turn affect permafrost thaw while longer periods of open water can impact heat transfer to the atmosphere. With recent climate warming in the Northern Hemisphere, there are general trends towards thinner ice and an increase in the length of the open water season. However, the paucity of in-situ observations in many countries has led to remote sensing playing an ever-increasing role for lake ice monitoring. This paper reviews progress in lake ice research conducted using active microwave remote sensing over the last 50 years and highlights areas where future developments are needed. Analysis of the literature found that the diversity of study areas where this work is taking place has increased, incorporating more lake sites from the mid-latitudes in North America, Europe, and Asia. Additionally, clear connections can be made between the launch of synthetic aperture radar (SAR) satellites and patterns in the use of certain radar frequencies in scientific studies. This review also discusses evolving theories concerning the scattering mechanisms associated with lake ice as well as the current state of lake ice cover and ice thickness retrieval methods. The paper concludes with a suite of recommendations for future research, highlighting work that is needed on: 1) advancing our understanding of the response of active microwave signals to lake ice properties; 2) applying machine learning algorithms to lake ice classification; 3) further exploring the retrieval of ice thickness from imaging SAR and radar altimetry data; and 4) other considerations such as data availability and the topic of snow on lake ice. • Active microwave remote sensing is the most common technology for studying lake ice. • Our understanding of how active data interacts with lake ice properties has changed. • A variety of methods exist for monitoring lake ice cover using SAR data. • Research is needed on interactions between active signals and lake ice properties. • Data availability will help develop algorithms to monitor essential ice properties. [ABSTRACT FROM AUTHOR]

In the event of a maritime oil spill in winter conditions, the fate of oil released under ice presents considerable difficulties with respect to detection, containment and recovery compared to spills that form slicks in open water. This study presents the first quantification of roughness and associated storage capacity of the ice-water interface for freshwater ice in the Laurentian Great Lakes in North America. Ground Penetrating Radar (GPR) observations are used to retrieve ice thicknesses every 2.5 cm on a grid over ice ranging between 0.26 and 0.48 m in the Straits of Mackinac to derive a digital elevation model of the ice bottom. The root mean square of height deviations of the ice underside is observed to be 0.03 m (over a correlation length of approximately 20 m) with a storage capacity of 0.03 m3/m2. The observed physical characteristics relevant to oil entrainment at the ice underside are similar to those reported for first year sea ice and may store similar quantities of oil during the initial spill. However, the difference in the physical structure between first year ice (with brine channels) and freshwater ice (consolidated ice cap) may result in a disparity in the timing of oil appearance, and require considerably different remediation treatment. • Ground Penetrating Radar is utilized to retrieve freshwater ice thickness (R 2 = 0.86). • Height deviations (roughness) of ice is 0.03 m over correlation length of ~20 m. • Storage capacity of fresh ice bottom is similar to first year sea ice (0.03 m3/m2). • Oil recovery methods for spills under sea ice may be applicable for freshwater. [ABSTRACT FROM AUTHOR]

The topic of satellite remote sensing of lake ice has gained considerable attention in recent years. Optical satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) allow for the monitoring of lake ice cover (an Essential Climate Variable or ECV), and dates associated with ice phenology (freeze-up, break-up, and ice cover duration) over large areas in an era where ground-based observational networks have nearly vanished in many northern countries. Ice phenology dates as well as dates of maximum and minimum ice cover extent (for lakes that do not form a complete ice cover in winter or do not totally lose their ice cover in summer) are useful for assessing long-term trends and variability in climate, particularly due to their sensitivity to changes in near-surface air temperature. Existing knowledge-driven (threshold-based) retrieval algorithms for lake ice cover mapping that use top-of-atmosphere (TOA) reflectance products do not perform well under lower solar illumination conditions (i.e. large solar zenith angles), resulting in low TOA reflectance. This research assessed the capability of four machine learning classifiers (i.e. multinomial logistic regression, MLR; support vector machine, SVM; random forest, RF; gradient boosting trees, GBT) for mapping lake ice cover, water and cloud cover during both break-up and freeze-up periods using the MODIS/Terra L1B TOA (MOD02) product. The classifiers were trained and validated using samples collected from 17 large lakes across the Northern Hemisphere (Europe and North America); lakes that represent different characteristics with regards to area, latitude, freezing frequency, and ice duration. Following an accuracy assessment using random k-fold cross-validation (k = 100), all machine learning classifiers using a 7-band combination (visible, near-infrared and shortwave-infrared) were found to be able to produce overall classification accuracies above 94%. Both RF and GBT provided overall and class-specific accuracies above 98% and a more visually accurate depiction of lake ice, water and cloud cover. The two tree-based classifiers offered the most robust spatial transferability over the 17 lakes and performed consistently well across ice seasons. However, only RF was relatively insensitive to the choice of the hyperparameters compared to the other three classifiers. The results demonstrate the potential of RF for mapping lake ice cover globally from MODIS TOA reflectance data. • This study assessed the capability of ML classifiers for lake ice mapping from MOIDS. • RF and GBT produced the best performance in terms of classification accuracies. • RF and GBT offered the most robust spatial and temporal transferability. • RF was insensitive to the choice of the hyperparameters compared to other classifiers. • The results show the potential of RF for mapping lake ice cover globally from MODIS. [ABSTRACT FROM AUTHOR]

The freeze/thaw state of permafrost landscapes is an essential variable for monitoring ecological, hydrological and climate processes. Ground surface state can be obtained from satellite data through time series analysis of C-band backscatter from scatterometer and Synthetic Aperture Radar (SAR) observations. Scatterometer data has been used in a variety of studies concerning freeze/thaw retrieval of the land surface. Coarse spatial resolution scatterometer data has great potential for application in this field due to its high temporal resolution (approx. daily observations). In this study, we investigate the influence of sub-grid cell (12.5 km) surface water (ice free and ice covered) on freeze/thaw retrieval based on ASCAT data using a threshold algorithm. We found discrepancies related to the surface water fraction in the detected timing of thawing and freezing of up to 2 days earlier thawing for spring and 3.5 days earlier freezing for autumn for open water fractions of 40% resulting in an overestimation of the frozen season. Results of this study led to the creation of a method for correction of water fraction impact on freeze/thaw data. Additionally, this study demonstrates the applicability of a new approach to freeze/thaw retrieval which has not so far been tested for SAR, specifically Sentinel-1. • Influence of sub-grid cell surface water on freeze/thaw retrieval based on ASCAT data • Discrepancies related to the surface water fraction in the detected timing of thawing and freezing • Found a general overestimation of the frozen season. • Study demonstrates applicability of approach which was so far not tested for SAR. [ABSTRACT FROM AUTHOR]

This study examines links between two sets of parameters obtained from Radarsat-2 Quad-pol data and geophysical information of lake ice and landfast sea ice in the Resolute Bay area, Nunavut, Canada. The first set includes the three orthogonal backscatter coefficients ( σ hh 0 , σ vv 0 , σ vh 0 ) in addition to the total backscatter power (SPAN). The second set includes polarimetric decomposition parameters obtained using Cloude-Pottier and Yamaguchi decomposition methods. Data were collected during the early freezing period from 20 September until 28 December 2017. Meteorological and ice climatological data from the weather station of Resolute Bay were used to facilitate this task. Ice thickness and salinity were obtained from established empirical models. Results show that single-bounce and multiple-bounce scattering mechanisms are active in lake ice during the entire freezing period while multiple-bounce scattering is active in sea ice only during the initial freezing. Backscatter parameters can be used to determine the thickness of lake ice up to any depth, while both backscatter and the tested decomposition parameters are related to the thickness of young sea ice (<30 cm thick). Physical explanations are offered in each case. Spatial maps of ice thickness are produced. Polarimetric decomposition parameters demonstrate erratic behavior throughout the development of lake ice. High entropy (>0.6) is found to be associated with comparable single- and multiple-bounce scattering mechanisms, hence can be used to identify new ice. Orthogonal backscatter coefficients and polarimetric decomposition parameters provide different information, yet their combination offers clues to better identify ice conditions and investigate questions about scattering mechanisms. • Backscatter parameters determine the thickness of lake ice up to any depth. • Backscatter parameters are related to the thickness of young sea ice. • Entropy and Alpha angle are related to the thickness of young sea ice. [ABSTRACT FROM AUTHOR]