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Ice plays key roles in regulating hydrological, ecological, biogeochemical, and socioeconomic functions of lakes. Long-term in situ lake ice phenological records indicate that lake ice is trending toward later freeze-up, earlier breakup, and a shorter ice duration. Parallel to study of lake ice using in situ records and process-based models, satellite remote sensing can expand our understanding of lake ice change over large spatial scales. However, most remote sensing studies have focused on large lakes or short periods of time, which may not robustly represent changes over multidecadal time periods or in the much more numerous small lakes. Here, we present a random forest model, Sensitive Lake Ice Detection (SLIDE), to accurately extract ice conditions from Landsat TM, ETM+, and OLI images. We trained the model using a manually labeled lake ice dataset (1089 labeled areas over 995 lakes globally). Our results show that our model achieves accurate classification between ice/snow and water (accuracy: 97.8%, kappa coefficient: 95.5%). Comparing Landsat-derived ice cover with in situ ice conditions, we show that our model produces less bias, lower RMSE, and higher kappa than does the Landsat snow/ice flag from the quality assessment band. This is especially true during the transitional period surrounding the ice on and off dates reported from in situ (mean bias -7.3% from our model, -17.3% from the Landsat quality band). Our results demonstrate the feasibility of mining the rich Landsat archive to study lake ice dynamics and of better flagging ice-affected lake observations.

Spectral albedo and light transmittance through snow, ice, and water were measured in Lake Wuliangsuhai (40 degrees 36 '-41 degrees 30 ' N, 108 degrees 43 '-108 degrees 70 ' E), Inner Mongolia, China, during winter 2016. Data on the weather, structure of lake ice, and geochemistry of water were also collected during the 60-day field program. The study lake is shallow (mean depth 1.0-1.5 m) with a large wetland area. Compared with polar lakes, solar elevation is higher, snow accumulation is much lower, and the ice has more sediment. The ice was all congelation ice with a mean thickness of 36.6 cm, corresponding to a mean air temperature of -9.6 degrees C. The mean daily broadband albedo and photosynthetically active radiation (PAR) band transmittance were 0.54 and 0.08 (bare ice), 0.74 and 0.04 (new snow), and 0.30 and 0.12 (melting period), respectively. The level of light allowed photosynthesis to occur to the bottom of the lake. The ice acted as a grey filter for the sunlight with a mean attenuation coefficient of 2.1 m(-1). These results expand our knowledge of the evolution of light transfer through ice and snow cover and its role in the ecology of lakes in temperate and arid areas.

Snow and ice were monitored by thermistor-string-based Snow and Ice Mass Balance Array (SIMBA) in Lake Orajärvi in northern Finland. An existing automatic SIMBA-algorithm was further developed to derive air/snow, snow/ice and ice/water interfaces based on the SIMBA environment temperature (ET) profiles. The identified interfaces agreed with in situ observations made in 2011/12 winter season. The method was capable to identify upward-moving snow/ice interface that was also visible from SIMBA heating temperature (HT) profiles, which responds to differences in the thermal diffusivities of air, snow, ice and water. The SIMBA data obtained in winters 2017/18 and 2018/19 were used to investigate snow and ice mass balance. An upward-moving snow/ice interface was detected as a result of meteoric ice (snow ice and superimposed ice) formation. Snow contributed to granular lake ice formation up to 40–55% of the total ice thickness on the seasonal mean. Heavy snowfalls and low air temperature in early winter are favourable for granular ice formation. The seasonal mean snow depth on nearby land was 2.7–2.9 times of that on the lake. The estimation of freeboard from snow and ice mass-balance measurement is sensitive to the snow density. Accurate ice freeboard calculation is still a challenge.

Ice Engineering is associated with how to solve challenges from different kinds of ice, such as, sea ice, river ice, lake ice, icing, and snow in cold regions. The aim is to design special structures which could resist structural and global impact from drifting ice and freezing ice.

Lakes sensitively respond to global and regional climate change, especially in arid areas. Using Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products, the lake ice phenology of 23 lakes in the Xinjiang Uygur Autonomous Region of China from 2001 to 2018 was extracted based on thresholds of ice/water pixel numbers, and their change trends over 18 years were calculated. The results of MODIS-derived lake ice phenology showed consistent variations with existing ice phenology data sets derived from passive microwave data. Generally, lakes in Xinjiang begin to freeze from October to December every year, and their ice cover periods end from March to June. The average ice cover duration for the 23 lakes is 167 days, of which 16 lakes have an average shortening rate of − 1.08 days/year and seven lakes have an average extending rate of 1.18 days/year. The majority of lakes experienced later freeze-up (17 lakes) and earlier break-up (18 lakes) from 2001 to 2018. Lake ice phenology is affected by both climatic factors and lake physicochemical characteristics, in which freeze-up dates are more easily affected by lake-specific factors such as lake area (r = 0.535), while climatic factors especially water surface temperature have greater impacts on lake break-up dates (r = − 0.874). Compared to air temperature, water surface temperature changes have a more direct influence on the variations in lake ice phenology, 1° increase in water surface temperature may cause the ice cover duration to decrease by 12 days, while precipitation changes have almost no effect on the lakes in Xinjiang. In some cases, lake changes such as changes to the area and mineralization may also have dominant impacts on lake ice phenology.

To improve the understanding of the seasonal evolution of the mass and heat budget of ice-covered lakes in the cold and arid climate zone, in-situ observations were collected during two winters (2016&ndash, 2017 and 2017&ndash, 2018) in Lake Wuliangsuhai, Inner Mongolia, China. The mean snow thickness was 5.2 and 1.6 cm in these winters, due to low winter precipitation. The mean ice thickness was 50.9 and 36.1 cm, and the ice growth rate was 3.6 and 2.1 mm day&minus, 1 at the lower boundary of ice. Analyses of mass and heat balance data from two winters revealed that the surface heat budget was governed by solar radiation and terrestrial radiation. The net heat flux loss of the ice was 9&ndash, 22 W m&minus, 2, affected by the snow and ice thickness. Compared to boreal lakes, Lake Wuliangsuhai received more solar radiation and heat flux from the water. The ice temperature had a strong diurnal variation, which was produced by the diurnal cycles of solar radiation, and air and water temperatures. These results expand our knowledge of the evolution of mass and heat balance in temperate lakes of mid-latitude arid areas.

Lake ice models can be used to study the latitudinal differences of current and projected changes in ice covered lakes under a changing climate. The Canadian Lake Ice Model (CLIMo) is a one-dimensional freshwater ice cover model that simulates Arctic and sub-Arctic lake ice cover well. Modelling ice cover in temperate regions has presented challenges due to the differences in composition between northern and temperate ice. This study presents a comparison of measured and modelled ice regimes, with a focus on refining CLIMo for temperate regions. The study sites include two temperate region lakes (MacDonald Lake and Clear Lake, Central Ontario) and two High Arctic lakes (Resolute Lake and Small Lake, Nunavut) where climate and ice cover information have been recorded over three seasons. The ice cover simulations were validated with a combination of time lapse imagery, field measurements of snow depth, snow density, ice thickness and albedo data, and historical ice records from the Canadian Ice Database (for Resolute Lake). Simulations of the High Arctic ice cover show good agreement with previous studies for ice-on and ice-off dates (MAE 6 to 8 days). Unadjusted simulations for the temperate region lakes show both an underestimation in ice thickness (~ 4 to 18 cm) and ice-off timing (~ 25 to 30 days). Field measurements were used to adjust the albedo parameterization used in CLIMo, which resulted in improvements to both simulated ice thickness, within 0.1 cm to 10 cm of manual measurements, and ice-off timing, within 1 to 7 days of observations. These findings suggest regionally specific measurements of albedo can improve the accuracy of lake ice simulations. These results further our knowledge regarding of the response of temperate and High Arctic lake ice regimes to climate conditions.

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.

Lakes play an important role in terrestrial ecosystems and have a significant impact on human production and life. Remote sensing techniques have been widely used to monitor changes in lakes. Several studies have been conducted pertaining to extraction of lake bodies using optical remote sensing images; however, owing to the influence of ice and snow in the lake and other parameters such as noise, the accuracy and stability of lake extraction still need to be improved. This study designed a new method by combing K-means clustering and the flood fill method (KCFFM) to improve the accuracy and stability of extraction results from complex backgrounds. Using Landsat 8 Operational Land Imager (OLI) data for five lakes in Central Asia, the accuracy and stability of KCFFM in summer and winter were evaluated and compared with those of five other methods. KCFFM showed the highest accuracy and stability among all the methods, especially during the lake ice period. For KCFFM, the Kappa coefficient was greater than 0.97 and 0.92 and the overall accuracy was greater than 99% and 98% in summer and winter, respectively. The area error rate of KCFFM was less than 3%, except for Lake Alakol (less than 10%). In addition, KCFFM significantly decreased the area error rate compared to other methods (1% to 15% in summer and 10% to 50% in winter). The proposed method optimized the time continuity, accuracy, and stability of lake body extraction. Thus, KCFFM can provide valuable basic data for monitoring lake water bodies.

Ice surface albedo is an important factor in various optical remote sensing technologies used to determine the distribution of snow or melt water on the ice, and to judge the formation or melting of lake ice in winter, especially in cold and arid areas. In this study, field measurements were conducted at Wuliangsuhai Lake, a typical lake in the semi-arid cold area of China, to investigate the diurnal variation of the ice surface albedo. Observations showed that the diurnal variations of the ice surface albedo exhibit bimodal characteristics with peaks occurring after sunrise and before sunset. The curve of ice surface albedo with time is affected by weather conditions. The first peak occurs later on cloudy days compared with sunny days, whereas the second peak appears earlier on cloudy days. Four probability density distribution functions-Laplace, Gauss, Gumbel, and Cauchy-were combined linearly to model the daily variation of the lake ice albedo on a sunny day. The simulations of diurnal variation in the albedo during the period from sunrise to sunset with a solar altitude angle higher than 5 degrees indicate that the Laplace combination is the optimal statistical model. The Laplace combination can not only describe the bimodal characteristic of the diurnal albedo cycle when the solar altitude angle is higher than 5 degrees, but also reflect the U-shaped distribution of the diurnal albedo as the solar altitude angle exceeds 15 degrees. The scale of the model is about half the length of the day, and the position of the two peaks is closely related to the moment of sunrise, which reflects the asymmetry of the two peaks of the ice surface albedo. This study provides a basis for the development of parameterization schemes of diurnal variation of lake ice albedo in semi-arid cold regions.

Satellite observations have revealed decreases in the duration of the seasonal snow and ice coverage of Great Slave Lake (GSL) and Great Bear Lake (GBL), large freshwater lakes in Northern Canada. However, limited information is available about ice thickness changes. Here, we present and validate a method to retrieve lake ice thickness using the CryoSat-2 (CS2) radar altimeter. These are the first satellite altimeter retrievals of lake ice thickness. Under optimal conditions, the CS2 signal is scattered from both the snow–ice and the ice–water interfaces, with returns from each interface being of sufficient power to be identified in the radar waveform. The distance between the scattering horizons is used to determine the ice thickness, similar to traditional ground penetrating radar measurements. The seasonal evolution of ice thickness of GBL and GSL is compared with in situ measurements, modeled ice thicknesses, and previous studies. The impact of ice and snow properties on signal penetration and the thickness retrieval are examined with synthetic aperture radar imagery. The CS2 ice thickness retrievals are able to observe the seasonal thickening of the lake ice and closely match the in situ measurements over both lakes ( $R > 0.65$ , $\text {RMSE} m). Thickness retrievals of thin ice are limited by a minimum waveform peak separation of 2 range bins, approximately 0.26 m in ice. Although not designed for lake ice studies, CS2 and future SAR satellite altimeter missions offer new possibilities to monitor the ice and water levels of climatically sensitive and influential lakes.

Lake ice phenology are considered as a sensitive indicator of global warming. In Tibetan Plateau (TP), differences exist in identifying lake ice phenology using remote sensing data because of the limited ground observation and difference in the sources of remote sensing and the available algorithms. In this study, four methods based on different remote sensed data (MODIS reflectance data, snow product, land surface temperature data, and microwave data) were used to identify the lake ice phenology of 18 lakes in the TP from 2002 to 2015. Lake ice duration was longer from the south to the north, and the highest consistency is occurred in monitoring the completely ice-covering duration. Temperature plays a greater role among several climatic-environmental elements. The lake ice period was shortened in the northeastern part of the TP, and extended in the middle and northern part.

Lake ice models are a vital tool for studying the response of ice-covered lakes to changing climates throughout the world. The Canadian Lake Ice Model (CLIMo) is a one-dimensional freshwater ice cover model that simulates Arctic and sub-Arctic lake ice cover well. Modelling ice cover in temperate regions has presented challenges due to the differences in ice composition between northern and temperate region lake ice. This study presents a comparison of measured and modelled ice regimes, with a focus on refining CLIMo for temperate regions. The study sites include two temperate region lakes (MacDonald Lake and Clear Lake, Central Ontario) and two High Arctic lakes (Resolute Lake and Small Lake, Nunavut) where climate and ice cover information have been recorded over three seasons. The ice cover simulations were validated with a combination of time lapse imagery, field measurements of snow depth, snow density, ice thickness and albedo data, and historical ice records from the Canadian Ice Database (for Resolute Lake). Simulations of High Arctic lake ice cover show good agreement with previous studies for ice-on and ice-off dates (MAE 6 to 8 days). Unadjusted simulations for the temperate region lakes show good ice-on timing, but an under-representation of ice thickness, and earlier complete ice-off timing (~3 to 5 weeks). Field measurements were used to adjust the albedo values used in CLIMo, which resulted in improvements to both simulated ice thickness (~3 cm MAE compared to manual measurements), and ice-off timing, within 0 to 7 days (2 days MAE) of observations. These findings suggest regionally specific measurements of albedo can improve the accuracy of lake ice simulations, which further our knowledge of the response of temperate and High Arctic lake ice regimes to climate conditions.

Ground-penetrating radar (GPR) is not commonly used to study lake ice, and in general, the ground-based use of radar frequencies greater than 500 MHz in cryosphere geophysics is rare, due to a general interest in deeper stratigraphy and the difficulty of extensive profiling over rough snow surfaces. Our goal was to find further information on the origin of the deposition and formation of intra-ice layers, bottom topography, and subbottom deposits using GPR with pulses centered near 850 MHz on two permanently ice-covered lakes in the McMurdo Dry Valleys, Antarctica. The profiles were obtained using a one-person sled operation over Lake Bonney, which is typical of lakes in the region, having an ice thickness that ranges between 3 and 5 m, and Lake Vida, where the maximum ice depth is at least 27 m. Lake Bonney exhibits a semicontinuous sediment horizon at approximately a 2-m depth and several minor horizons. In contrast, Lake Vida contains unconformably eroded and folded continuous reflection horizons, packages of minor horizons between major horizons, evidence of incised fluvial deposition along the bottom, and subbottom penetration of at least 4.5 m in some areas. Where the ice thickness is less than 20 m, the lake is frozen to the bottom. Most horizon waveform phase attributes indicate relatively lower permittivity than in the surrounding matrix. Consequently, we interpreted these strata to be caused by layers of pure ice embedded within a salty and dirty ice matrix, which were formed during minor flooding. These findings supported previous conclusions that Lake Vida ice formed from surface runoff in combination with periods of ablation.

High Resolution Limited Area Model (HIRLAM), used for operational numerical weather prediction in the Finnish Meteorological Institute (FMI), includes prognostic treatment of lake surface state since 2012. Forecast is based on the Freshwater Lake (FLake) model integrated to HIRLAM. Additionally, an independent objective analysis of lake surface water temperature (LSWT) combines the short forecast of FLake to observations from the Finnish Environment Institute (SYKE). The resulting description of lake surface state – forecast FLake variables and analysed LSWT – was compared to SYKE observations of lake water temperature, freezing and melting dates as well as the ice and snow thickness for 2012–2018 over 45 lakes in Finland. During the ice-free period, the predicted LSWT corresponded to the observations with a slight overestimation, with a systematic error of +0.91 K. The colder temperatures were underrepresented and the maximum temperatures were too high. The objective analysis of LSWT was able to reduce the bias to +0.35 K. The predicted freezing dates corresponded well the observed dates, mostly within the accuracy of a week. The forecast melting dates were far too early, typically several weeks ahead of the observed dates. The growth of ice thickness after freezing was generally overestimated. However, practically no predicted snow appeared on lake ice. The absence of snow, found to be due to a technical error in HIRLAM, is suggested to be also the reason of the inaccurate simulation of the ice melt in spring.

Thermokarst lakes are important emitters of methane, a potent greenhouse gas. However, accurate estimation of methane flux from thermokarst lakes is difficult due to their remoteness and observational challenges associated with the heterogeneous nature of ebullition. We used high-resolution (9–11 cm) snow-free aerial images of an interior Alaskan thermokarst lake acquired 2 and 4 days following freeze-up in 2011 and 2012, respectively, to detect and characterize methane ebullition seeps and to estimate whole-lake ebullition. Bubbles impeded by the lake ice sheet form distinct white patches as a function of bubbling when lake ice grows downward and around them, trapping the gas in the ice. Our aerial imagery thus captured a snapshot of bubbles trapped in lake ice during the ebullition events that occurred before the image acquisition. Image analysis showed that low-flux A- and B-type seeps are associated with low brightness patches and are statistically distinct from high-flux C-type and hotspot seeps associated with high brightness patches. Mean whole-lake ebullition based on optical image analysis in combination with bubble-trap flux measurements was estimated to be 174 ± 28 and 216 ± 33 mL gas m−2 d−1 for the years 2011 and 2012, respectively. A large number of seeps demonstrated spatiotemporal stability over our 2-year study period. A strong inverse exponential relationship (R2 > = 0.79) was found between the percent of the surface area of lake ice covered with bubble patches and distance from the active thermokarst lake margin. Even though the narrow timing of optical image acquisition is a critical factor, with respect to both atmospheric pressure changes and snow/no-snow conditions during early lake freeze-up, our study shows that optical remote sensing is a powerful tool to map ebullition seeps on lake ice, to identify their relative strength of ebullition, and to assess their spatiotemporal variability.

We have conducted a multiyear (2011–2014) airborne microwave radiometer measurement program of snow on lake ice, regularly using our HUTRAD instrument (6.9 to 36.5 GHz, vertical and horizontal polarization). Occasionally, we have also used our interferometric 1.4 GHz HUT-2D radiometer. Our data consist of observations along a 4-km test line over two lakes in southern Finland, and include data also for adjacent land areas, whose land-cover types range from forest to short vegetation. A wide variety of snow and ice conditions were encountered, covering early winter, mid-winter and late winter conditions, and the melting season. In this paper we focus on the comparison of brightness temperatures observed for snow on lake ice and snow on terrain.

Most high-altitude lakes are more sensitive to global warming than the regional atmosphere. However, most existing climate models produce unrealistic surface temperatures on the Tibetan Plateau (TP) lakes, and few studies have focused on the influence of ice surface albedo on high-altitude lakes. Based on field albedo measurements, moderate resolution imaging spectrometer (MODIS) albedo products and numerical simulation, this study evaluates the ice albedo parameterization schemes in existing lake models and investigates the characteristics of the ice surface albedo in six typical TP lakes, as well as the influence of ice albedo error in the FLake model. Compared with observations, several ice albedo schemes all clearly overestimate the lake ice albedo by 0.26 to 0.66, while the average bias of MODIS albedo products is only 0.07. The MODIS-observed albedo of a snow-covered lake varies with the snow proportion, and the lake surface albedo in a snow-free state is approximately 0.15 during the frozen period. The MODIS-observed ice surface (snow-free) albedos are concentrated within the ranges of 0.14–0.16, 0.08–0.10 and 0.10–0.12 in Aksai Chin Lake, Nam Co Lake and Ngoring Lake, respectively. The simulated lake surface temperature is sensitive to variations in lake ice albedo especially in the spring and winter.

This bibliography includes 294 books and articles published mostly in 2010. Annotated books and articles relate to ten thematic sections of glaciology: 1. General problems of glaciology 2. Physics and chemistry of ice 3. Ice in atmosphere 4. Snow cover 5. Snow avalanches and glacial mudflows 6. Sea ice 7. River and lake ice 8. Icings and ground ice 9. Glaciers and ice sheets 10. Paleoglaciology

Dual-polarized airborne passive microwave (PM) brightness temperatures (Tb) at 6.9 GHz H/V, 19 GHz H/V and 37 GHz H/V and spaceborne active microwave (AM) X-band (9.65 GHz VV, VH) backscatter (σ0) are observed coincident to in situ snow and lake-ice measurements collected over two lakes near Inuvik, Canada. Lake-ice thickness is found to be positively correlated with 19 GHz V emission (R = 0.67) and negatively with 19 GHz H emission (R = −0.79), indicating surface ice conditions influence microwave interaction. Lake ice types are delineated from TerraSAR-X synthetic aperture radar (SAR) images using the iterative region growing with semantics (IRGS) segmentation algorithm implemented in the MAGIC (MAp Guided Ice Classification) system. The spatial extent of derived ice type classes correspond well to in situ observations. The overall magnitude of emission at 19 GHz H and X-band VH σ0 increase with the scattering potential of associated ice types (grey/rafted ice). Transects of 6.9 GHz PM and 19 GHz PM exhibit positive relationships with VH σ0 over freshwater lake ice, with the greatest R coefficients at H-pol (R = 0.64, 0.46). Conversely, 6.9 GHz Tb and 19 GHz Tb exhibit negative R coefficients in regions of brackish water due to tubular bubble and brine inclusions in the ice. This study identifies congruency between PM and AM scattering mechanisms over lake ice for the purpose of identifying the influence of ice types on overall microwave interaction within the lake-ice system.

Two experimental brightness temperature data sets are revisited: A ground-based campaign of terrestrial snow in 1985 and an airborne research program of snow on lake ice in 2007–2014. Both data sets were obtained in southern Finland using multi-frequency microwave radiometers. In this paper both the overall behavior of the brightness temperature during the ground-based campaign and results from the intensive data collection period on April 12–13 (melt/freeze cycle) are discussed. Additionally, selected results from the airborne snow/lake ice program are compared with theoretical values. Two theoretical models are used for simulation.

International audience; The radar backscatter response of snow covered lake and fjord ice is investigated using a ground based multifrequency synthetic aperture radar system operated in a tomographic configuration. Direct imaging of the snow and ice layering is achieved by focusing the signal from a two-dimensional (2-D) synthetic array in the 3-D space. A mathematical derivation describing the propagation of electromagnetic waves across a dense and multilayered complex medium with arbitrary but finite number of layers is presented. It is used to estimate the depth and refractive indices of the snow and ice layers from the tomograms employing a simple least-square optimization scheme. The lake and fjord ice datasets are compared with respect to their vertical stratification and estimated refractive indices. The vertical structure of the reflectivity of the snow-covered lake ice is investigated and compared at two different frequencies, X-, and C-band. It is found that snow and ice volume responses at C-band are very low compared to the corresponding responses at X-band. At both frequencies, backscattering from surface and interface structures dominate volume contributions.

Albedo products (MOD10A1, MYD10A1, and MCD43A3) from the Moderate Resolution Imaging Spectrometer (MODIS) have the potential to be integrated directly into lake ice models such as the Canadian lake ice model (CLIMo) to improve the simulation of freshwater break-up (ice-off). The high albedo of snow and lake ice has been shown to affect the timing of break-up. Therefore, the surface energy balance parameterization of CLIMo requires accurate estimates of albedo when modelling phenology. MOD10A1, MYD10A1, and MCD43A3 were evaluated against in situ snow and ice albedo observations taken over a partially snow-covered freshwater lake (Malcolm Ramsay Lake) near Churchill, Manitoba, during the ice growth period (15 February 2012 to 25 April 2012). The MODIS albedo products were then compared with the CLIMo's albedo parameterization during the ice break-up period. The MODIS albedo products MOD10A1, MYD10A1, and MCD43A3 retrieved snow and ice albedo with root mean square error values of 0.07, 0.08, and 0.06, respectively, compared with spatially averaged in situ albedo measurements during ice growth. MODIS albedo products compared with CLIMo's melting ice parameterization during the melt season indicate that CLIMo's albedo estimates have a mean difference of at least 0.14 compared with the MODIS retrievals during melt. The quality of the albedo retrievals over lake ice from MODIS and the need for more accurate albedo simulations during the melt season suggest that the assimilation of MODIS albedo products into CLIMo could be beneficial for the determination of break-up (ice-off) dates. Copyright © 2014 John Wiley & Sons, Ltd.

The interannual variability of ice and snow of small shallow Lake Vendyurskoe (Karelia, Russia) is investigated on the basis of long-term records (1994-2011). The water temperature, thickness of the snow and ice, as well as incident, reflected and penetrating through the ice fluxes of solar radiation were registered. The lake surface albedo in spring varied widely from 0.95 (fresh snow, sunny weather) to 0.1 (water on the ice surface). The heat balance of the lake surface (solar and effective long-wave radiation, evaporation and condensation, turbulent heat transfer) was estimated for the period of melting. The amount of heat accumulated in the sub-ice layer during the spring convection was assessed. Estimates of the proportion of solar radiation on the melting of ice and water heating in the under-ice layer during the spring thaw were carried out.

A 20-km regional climate model, the Abdus Salam International Centre for Theoretical Physics Regional Climate Model version 4 (ICTP RegCM4), is employed to investigate heavy lake-effect snowfall (HLES) over the Great Lakes Basin and the role of ice cover in regulating these events. When coupled to a lake model and driven with atmospheric reanalysis data between 1976 and 2002, RegCM4 reproduces the major characteristics of HLES. The influence of lake ice cover on HLES is investigated through 10 case studies (2 per Great Lake), in which a simulated heavy lake-effect event is compared with a companion simulation having 100% ice cover imposed on one or all of the Great Lakes. These experiments quantify the impact of ice cover on downstream snowfall and demonstrate that Lake Superior has the strongest, most widespread influence on heavy snowfall and Lake Ontario the least. Ice cover strongly affects a wide range of atmospheric variables above and downstream of lakes during HLES, including snowfall, surface energy fluxes, wind speed, temperature, moisture, clouds, and air pressure. Averaged among the 10 events, complete ice coverage causes major reductions in lake-effect snowfall (>80%) and turbulent heat fluxes over the lakes (>90%), less low cloudiness, lower temperatures, and higher air pressure. Another important consequence is a consistent weakening (30%–40%) of lower-tropospheric winds over the lakes when completely frozen. This momentum reduction further decreases over-lake evaporation and weakens downstream wind convergence, thus mitigating lake-effect snowfall. This finding suggests a secondary, dynamical mechanism by which ice cover affects downstream snowfall during HLES events, in addition to the more widely recognized thermodynamic influence.

An automated ice-mapping algorithm has been developed and evaluated using data from the GOES-13 imager. The approach includes cloud-free image compositing as well as image classification using spectral criteria. The algorithm uses an alternative snow index to the Normalized Difference Snow Index (NDSI). The GOES-13 imager does not have a 1.6 µm band, a requirement for NDSI; however, the newly proposed Mid-Infrared Sea and Lake Ice Index (MISI) incorporates the reflective component of the 3.9 µm or mid-infrared (MIR) band, which the GOES-13 imager does operate. Incorporating MISI into a sea or lake ice mapping algorithm allows for mapping of thin or broken ice with no snow cover (nilas, frazil ice) and thicker ice with snow cover to a degree of confidence that is comparable to other ice mapping products. The proposed index has been applied over the Great Lakes region and qualitatively compared to the Interactive Multi-sensor Snow and Ice Mapping System (IMS), the National Ice Center ice concentration maps and MODIS snow cover products. The application of MISI may open additional possibilities in climate research using historical GOES imagery. Furthermore, MISI may be used in addition to the current NDSI in ice identification to build more robust ice-mapping algorithms for the next generation GOES satellites.

We have conducted a multiyear airborne multifrequency microwave radiometer data collection program over a test site comprising two lakes and adjacent land areas in southern Finland. A variety of snow and ice conditions were encountered, covering early winter, mid-winter, and late winter conditions, and the melting season. A semi-empirical radiative transfer forward model has been used for simulations and the theoretical values have been compared with experimental brightness temperature observations.

Lakes are abundant throughout the pan-Arctic region. For many of these lakes ice cover lasts for up to two thirds of the year. The frozen cover allows human access to these lakes, which are therefore used for many subsistence and recreational activities, including water harvesting, fishing, and skiing. Safe traveling condition onto lakes may be compromised, however, when, after significant snowfall, the weight of the snow acts on the ice and causes liquid water to spill through weak spots and overflow at the snow-ice interface. Since visual detection of subsnow slush is almost impossible our understanding on overflow processes is still very limited and geophysical methods that allow water and slush detection are desirable. In this study we demonstrate that a commercially available, lightweight 1 GHz, ground penetrating radar system can detect and map extent and intensity of overflow. The strength of radar reflections from wet snow-ice interfaces are at least twice as much in strength than returns from dry snow-ice interface. The presence of overflow also affects the quality of radar returns from the base of the lake ice. During dry conditions we were able to profile ice thickness of up to 1 m, conversely, we did not retrieve any ice-water returns in areas affected by overflow.