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The Qinghai-Xizang Plateau， distinguished by its vast array of lakes， exhibits marked seasonal lake ice coverage， which is highly responsive to climatic shifts.This ice coverage plays a crucial role in the dynamic interchange of fluxes between the lake surfaces and the atmosphere.Despite the significance of these ice phenomena， the limited availability of extensive， long-term observational data on plateau lake ice has led to a reliance on reanalyzed ice datasets， particularly ERA5-Land.This study aims to rigorously evaluate the effectiveness and potential enhancements of ERA5-Land's lake ice data in the distinct environment of the Qinghai-Xizang Plateau.Focusing on data collected from 2010 to 2022 for Qinghai Lake and Ngoring Lake， this research meticulously examines the ERA5-Land reanalysis data's ability to accurately capture the intrinsic characteristics of plateau lake ice.The study uncovered that ERA5-Land tends to overestimate the ice thickness by about 0.54~0.62 m and erroneously prolongs the freezing period by roughly 68 days per year for these lakes.This notable discrepancy necessitated an in-depth error analysis， which synthesized ERA5-Land data with direct observational data from Ngoring Lake， revealing that inaccuracies primarily originated from the FLake one-dimensional lake model within the ERA5-Land system.In an effort to address these inaccuracies， the study employed the MCD43A3 surface albedo product for both Qinghai Lake and Ngoring Lake over the same period.This innovative approach significantly refined the FLake model by incorporating both a multi-year average albedo and a dynamic daily average albedo.These methodological improvements led to a substantial reduction in the average bias of ice thickness， by 85% and 90% respectively， and narrowed the deviation in the modeled freezing period by about 6 and 8 days per year.The enhancements were particularly notable in lakes with longer periods of snow cover， where the dynamic albedo adjustment proved to be highly effective.This research has successfully identified the albedo parameter within the FLake model as a key source of error in ERA5-Land's lake ice characterizations and has implemented practical adjustments to rectify this.These enhancements have markedly increased the model's precision in simulating lake ice， thereby significantly improving the accuracy of ERA5-Land reanalyzed lake ice data.This advancement is particularly pertinent for the unique climatic and geographical conditions of Qinghai Lake and Ngoring Lake on the Qinghai-Xizang Plateau and offers invaluable insights for future research and practical applications in this domain.The findings of this study contribute profoundly to our understanding and modeling of lake ice phenomena in high-altitude regions and have broader implications for climatological research and environmental monitoring on the Qinghai-Xizang Plateau.

Phenological changes are of great significance to the study of climate response and ecological environment.Based on the MODIS V6 snow product and reflectivity product in the past 20 years， the snow and lake ice phenology in the Qinghai Lake Basin were obtained， and the spatial distribution characteristics of the two were analyzed.On this basis， Theil-Sen Median method and linear regression method were used to analyze the variation trend of snow phenology and lake ice phenology， as well as the correlation between them in low altitude areas.The results show that： （1） Freeze-up start， Break-up start and Exist Duration of lake ice in Qinghai Lake are in the range of 321~389 d， 425~464 d and 0~174 d， respectively.On the whole， Freeze-up start and Break-up start of lake ice were delayed， and the delay rates were 0.3 d·a-1 and 0.2 d·a-1， respectively.Exist Duration of lake ice showed a shortening trend， with a shortening rate of 0.6 d·a-1.There is a significant correlation between lake ice phenology and longitude.From east to west， Freeze-up start is postponed， Break-up start is advanced， and Exist Duration of lake ice is shortened.（2） Start of snow cover days， End of snow cover days and Snow cover days in Qinghai Lake Basin are distributed in the range of 275~404 d， 353~484 d and 3~209 d， respectively.Among them， start of snow cover days and End of snow cover days showed an early trend and a delayed trend respectively， and the change rates were 0.8 d·a-1 and 0.11 d·a-1 respectively.Snow cover days showed an increasing trend， with a growth rate of 0.6 d·a-1.Snow phenology is closely related to altitude.With the increase of altitude， start of snow cover days is advanced， End of snow cover days is delayed， and Snow cover days increases.（3） Air temperature and negative accumulated temperature in winter are important factors affecting lake ice phenology.With the increase of temperature and negative accumulated temperature in winter， Freeze-up start will be delayed， Break-up start will be advanced， and Exist Duration of lake ice will be shortened.For snow phenology， there is a significant negative correlation between Snow cover days and the temperature.The temperature decreases and Snow cover days increases.（4） There is a potential relationship between some snow cover and lake ice phenology parameters in low-altitude watersheds.There is a significant negative correlation between the beginning date of snow cover and the beginning date of lake ice freezing， and the correlation coefficient is -0.404.As the lake surface insulation layer， the increase of snow cover days will also greatly slow down the speed of lake ice melting， resulting in the delay of lake ice melting date.Therefore， there is a positive correlation between the two， and the correlation coefficient is 0.349.The change law of ecosystem in the basin revealed by this study is of positive significance to the local ecosystem， and can provide theoretical basis and technical support for the environmental monitoring of Qinghai Lake Basin.

The perennially ice-covered lakes of the McMurdo Dry Valleys (MDVs), Antarctica, are an important reservoir of liquid water in an arid and largely frozen environment. During the austral summer, the margins of these ice covers melt, forming a “moat” of liquid water and thin ice, allowing exchange between lake waters and the atmosphere to occur and serving as an interface between lake, soil, and stream ecosystems. The size of these moats varies from year to year. Here, we have established the first published record of moat area changes at MDVs’ Lake Fryxell through time using manual traces of the moat as observed via satellite imagery. We have also tested a semi-automated approach for measuring moat area and found that it consistently underestimated the manual record, which we suspect may be due to the lower spatial resolution of images used in this versus the manual approach. Finally, we developed a predictive model based on readily available climate data, allowing moat area to be predicted beyond the limits of the satellite-based records. We found that functional moat area varies annually, potentially influencing ecosystem processes in the moats.

Arctic landscapes are characterized by diverse water bodies, which are covered with ice for most of the year. Ice controls surface albedo, hydrological properties, gas exchange, and ecosystem services, but freezing processes differ between water bodies. We studied the influence of geomorphology and meteorology on winter ice of water bodies in the Lena Delta, Siberia. Electrical conductivity (EC) and stable water isotopes of ice cores from four winters and six water bodies were measured at unprecedented resolution down to 2-cm increments, revealing differences in freezing systems. Open-system freezing shows near-constant isotopic and EC gradients in ice, whereas closed-system freezing shows decreasing isotopic composition with depth. Lena River ice displays three zones of isotopic composition within the ice, reflecting open-system freezing that records changing water sources over the winter. The isotope composition of ice covers in landscape units of different ages also reflects the individual water reservoir settings (i.e., Pleistocene vs. Holocene ground ice thaw). Ice growth models indicate that snow properties are a dominant determinant of ice growth over winter. Our findings provide novel insights into the winter hydrochemistry of Arctic ice covers, including the influences of meteorology and water body geomorphology on freezing rates and processes.

This article introduces the first use of global navigation satellite system (GNSS) reflectometry for monitoring lake ice phenology. This is demonstrated using Qinghai Lake, Tibetan Plateau, as a case study. Signal-to-noise ratio (SNR) values obtained from the cyclone GNSS (CYGNSS) constellation over four ice seasons (2018 to 2022) were used to examine the impact of lake surface conditions on reflected GNSS signals during open water and ice cover seasons. A moving t-test algorithm was applied to time-varying SNR values allowing for the detection of lake ice at daily temporal resolution. Good agreement was achieved between ice phenology records derived from CYGNSS data and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The CYGNSS timings for freeze-up, i.e., the period starting with the first appearance of ice on the lake (freeze-up start; FUS) until the lake becomes fully ice covered (freeze-up end; FUE), as well as those for breakup, i.e., the period beginning with the first pixel of open water (breakup start; BUS) and ending when the whole lake becomes ice-free (breakup end; BUE), were validated against the phenology dates derived from MODIS images. Mean absolute errors are 7, 5, 10, 4, and 5 days for FUS, FUE, BUS, BUE, and ice cover duration, respectively. Observations revealed the sensitivity of GNSS reflected signals to surface melt prior to the appearance of open water conditions as determined from MODIS, which explains the larger difference of 10 days for BUS.

The rapid formation of glacial lakes is one of the most conspicuous landscape changes caused by atmospheric warming in glacierised regions. However, relatively little is known about the history and current state of glacial lakes in the High Arctic. This study aims to address this issue by providing the first inventory of glacial lakes in Svalbard, focusing in particular on the post-Little Ice Age evolution of glacial lakes and their typology. To do so, we used aerial photographs and satellite imagery together with archival topographic data from 1936 to 2020. The inventory comprises the development of 566 glacial lakes (146 km2) that were still in direct contact with glaciers during the period 2008–2012. The results show a consistent increase in the total area of glacial lakes from the 1930s to 2020 and suggest an apparent link between climatic and geological factors, and the formation of specific lake dam types: moraine, ice, or bedrock. We also detected 134 glacial lake drainage events that have occurred since the 1930s. This study shows that Svalbard has one of the highest rates of glacial lake development in the world, which is an indicator of the overall dynamics of landscape change in the archipelago in response to climate change.

Many lakes are widely and densely distributed on the Qinghai-Tibet Plateau.Due to the high altitude， the lakes seasonally freeze with a long ice-covered period.Lake ice can significantly affect the local and regional climate and the sub-glacial aquatic ecosystem by changing the transfer process of radiation energy on and in the lakes.However， their characteristics during the ice-covered period still remain unclear.Therefore， from February 6 to 28， 2022， we conducted a systematic observation of water ice air in Qinghai Lake， the largest lake on the Qinghai-Tibet Plateau.Using field observation data， video images and the precipitation data of peripheral observation stations， the influence of different ice cover conditions on the radiation and temperature of the water-ice-atmosphere on Qinghai Lake during the ice-covered period was analyzed.Results showed that different weather processes such as snowfall， sand and gale could remarkably change the property of cover on the ice， leading to variations of ice thickness on Qinghai Lake.Furthermore， the discrepancies of solar radiation absorption and reflection of different covers result in energy equilibrium that directly dictate ice surface temperature at water-ice-atmosphere interface.Bare ice with low albedo and low absorption induced the relatively strong net solar radiation and the correspondingly large diurnal variations of ice temperature.Compared to bare ice， the high reflection of snow cover and the strong absorption of sand and dust make the ability of net short-wave radiation to penetrate ice weaker， and the diurnal variation of lake ice temperature is smaller.

We present a Simple Lake Ice Model to calculate the growth rate of lake ice in a cold and relatively dry climate. The focus is on Lake St. Moritz, Switzerland, which has an area of 0.78 km2 and is about 45 m deep. In winter the lake is extensively used for recreational purposes, including horse racing with thousands of spectators. Safety on the ice cover is essential, and there is a great need to have a simple tool with which the growth rate of the ice layer can be calculated for given meteorological conditions. The approach is based on a simple formulation of the upper temperature of the ice layer, which depends on air temperature and snow cover. Input data are the date on which the lake freezes over, daily mean air temperatures and snow depth. For the winter 2021/22 calculated ice growth compares well with ice thickness measurements. We demonstrate that grooming of the snow has a significant positive effect on the ice thickening rate. We also evaluate the sensitivity of the simulated ice thickness to increasing mean temperature.

Lake ice changes in winter under the influence of global climate change, but how lake ice changes will regulate water gross primary productivity (GPP) and carbon sequestration capacity is still unclear. Here, we evaluated and analyzed the geographic spatial pattern and dynamic changes of lake ice and GPP on the Qinghai-Tibetan Plateau (QTP) in the past 20 years. Results show that lake ice duration on the QTP is 123.36±2.43 d on average, although longer for lakes at higher altitudes, of moderate size, and with shallower depths. Lake ice thickness is between 55–66 cm on average, and its GPP on the QTP is between 0.17–3.35 g C m−2 d−1. In the context of global climate change, reductions in lake ice cover duration and changes in ice thickness on the QTP increased phytoplankton GPP during the winter freeze period while decreasing Carbon dioxide (CO2) emissions during the melting period.

Lakes on the Qinghai Tibet Plateau (QTP) are widely distributed spatially, and they are mostly seasonally frozen. Due to global warming, the thickness and phenology of the lake ice has been changing, which profoundly affects the regional climate evolution. There are a few studies about lake ice in alpine regions, but the understanding of climatological characteristics of lake ice on the QTP is still limited. Based on a field experiment in the winter of 2022, the thermal conductivity of Qinghai Lake ice was determined as 1.64 W·m−1·°C−1. Airborne radar ice thickness data, meteorological observations, and remote sensing images were used to evaluate a quasi-steady ice model (Leppäranta model) performance of the lake. This is an analytic model of lake ice thickness and phenology. The long-term (1979–2017) ice history of the lake was simulated. The results showed that the modeled mean ice thickness was 0.35 m with a trend of −0.002 m·a−1, and the average freeze-up start (FUS) and break-up end (BUE) were 30 December and 5 April, respectively, which are close to the field and satellite observations. The simulated trend of the maximum ice thickness from 1979 to 2017 (0.004 m·a−1) was slightly higher than the observed result (0.003 m·a−1). The simulated trend was 0.20 d·a−1 for the FUS, −0.34 d·a−1 for the BUE, and −0.54 d·a−1 for the ice duration (ID). Correlation and detrending analysis were adopted for the contribution of meteorological factors. In the winters of 1979–2017, downward longwave radiation and air temperature were the two main factors that had the best correlation with lake ice thickness. In a detrending analysis, air temperature, downward longwave radiation, and solar radiation contributed the most to the average thickness variability, with contributions of 42%, 49%, and −48%, respectively, and to the maximum thickness variability, with contributions of 41%, 45%, and −48%, respectively. If the six meteorological factors (air temperature, downward longwave radiation, solar radiation, wind speed, pressure, and specific humidity) are detrending, ice thickness variability will increase 83% on average and 87% at maximum. Specific humidity, wind, and air pressure had a poor correlation with ice thickness. The findings in this study give insights into the long-term evolutionary trajectory of Qinghai Lake ice cover and serve as a point of reference for investigating other lakes in the QTP during cold seasons.

Study region: This study analyzed lake surface temperature (LST) trends and spatial distribution across 535 predominantly small to medium lakes across the North Slave Region (NSR) of the Northwest Territories (NWT), Canada. Study focus: The NWT is characterized by a vast number of lakes covering a significant portion of its spatial extent. However, there is limited knowledge of how LST responds to climate warming in this region. To address this, LST was analyzed in four distinct periods: open water season (OW), ice cover season (IC), and the transitional months of May (TM) and October (TO). LSTs from 1984 to 2021 were retrieved from a lake-specific satellite-derived LST dataset (North Slave LST). LST trend distribution and relationships were analyzed using the Mann-Kendall test and a multilinear regression model. New hydrological insights: The analysis revealed an overall increase in LST, with average rates (max) of 0.03 °C/year (0.05 °C/year), 0.03 °C/year (0.06 °C/year), and 0.13 °C/year (0.27 °C/year) for OW, TM, and TO, respectively accross study lakes. A faster rate of change was observed in October compared to other periods. Results indicated significant increases in LST for 411 lakes (77%) during OW, 418 lakes (78%) during TO, and 490 lakes (92%) during TM. The spatial distribution and magnitude of LST change were primarily influenced by geographical than morphometric properties. The analysis demonstrated later freeze-up (0.20 day/year) and earlier break-up (−0.17 day/year) of lake ice across the NSR.

Khovsgol Lake, a seasonal ice-covered lake located in boreal Siberia, plays important economic, transportation and agricultural roles for local residents. On 11 January 2021, an Mw 6.7 earthquake struck the center of this lake, providing a unique opportunity to better understand lake ice movement, ridge distribution, and hydraulic and hydrodynamic processes, as a result of the mainshock. We use a pixel-tracking method on both Sentinel-1 synthetic aperture radar and Sentinel-2 optical remote-sensing data covering this lake before, during and after the mainshock to obtain lake-ice displacement. Combining these measurements with different viewing geometries, we determine the locations of pressure ridges in each period and derive their relative 3D displacement maps with a precision of

Global surface freshwater primarily resides in lakes, with the overwhelming majority found in Earth’s largest lakes, thus understanding potential climate change effects in these large lakes is critical. In dimictic lakes, climate change has extended the duration of summer thermal stratification and reduced the length of the ice season. These changes are relatively straightforward to evaluate in smaller, inland lakes. However, in large lakes, such as the North American Great Lakes, temporally intermittent and spatially heterogeneous ice cover, and spatial thermal heterogeneity limit the utility of simple ice on–off or mixing classifications; therefore, assessing how climate change is impacting winter conditions in large lakes is challenging. Here, we use in-situ and satellite-derived surface water temperature observations from the North American Great Lakes to overcome these limitations and show that warming air temperatures are driving reductions in the number of winter days, collectively those with either ice cover or inverse thermal stratification, in favor of increases in isothermal conditions for the period 1995–2023. We find that on average the Great Lakes are experiencing a loss of 14 winter days per decade. Our results demonstrate how climate change has yielded disproportionate changes in the annual thermal cycle and mixing conditions of Earth’s largest freshwater system and signals the potential for fundamental ecosystem shifts due to a loss of winter.

Lake ice thickness (LIT) is one of the key climate variables in the lake ice domain, but there are currently large uncertainties in the retrieval of LIT. We present and validate a new LIT retrieval method that utilizes ICESat-2 data to assist CryoSat-2 echo peak selection, aiming to improve the accuracy of LIT retrieval and enable data acquisition without on-site measurements. The method involves screening out similar ICESat-2 and CryoSat-2 tracks based on time and space constraints. It also involves dynamically adjusting the range constraint window of CryoSat-2 waveforms based on the high-precision lake ice surface ellipsoid height obtained from ICESat-2/ATL06 data. Within this range constraint window, the peak selection strategy is used to determine the scattering interfaces between snow-ice and ice-water. By utilizing the distance between the scattering horizons, the thickness of the lake ice can be determined. We performed the ice thickness retrieval experiment for Baker Lake in winter and verified it against the on-site measurement data. The results showed that the accuracy was about 0.143 m. At the same time, we performed the ice thickness retrieval experiment for Great Bear Lake (GBL), which does not have on-site measurement data, and compared it with the climate change trend of GBL. The results showed that the retrieval results were consistent with the climate change trend of GBL, confirming the validity of the proposed method.

The texture, structure, ionic and trace element composition of samples of fast (coastal-sea) and lake ice collected in 2014 in the area of Cape Marre-Sale (the North-Western Siberia) were analyzed. The following main types of the ice structure were identified in ice sections: firn ice with randomly oriented small crystals; lake large- and small-crystalline bubble ice; layered fast sea ice with small isometric and vertically elongated crystals. The upper part of the lake ice is formed by recrystallized snow containing marine aerosols and lake water. The coefficient of involvement of the main ions from the solution during the ice formation varies for lake ice from 0.02 to 1.51, for sea ice – from 0.10 to 0.23, and for coastal-marine - from 0.03 to 0.04. The difference in the degree of ion involvement into the lake ice is related to the sources of components entering the process of formation of firn and large ice crystals from lake water. Coastal sea ice has high concentrations of trace elements relative to the clarks of sea waters. The income of trace elements into the coastal sea ice is probably determined by continental runoff. It is established that the mineralization of seasonal ice increases with a decrease in the size of crystals. The dependence of the values of the Europium anomaly on the rate of ice formation was revealed. The Europium anomaly in coastal sea ice is inherited from seawater, and the upper part of lake ice is inherited from precipitation.

Satellite altimetry has been proven to measure water levels in lakes and rivers effectively. The Sentinel-3A satellite is equipped with a dual-frequency synthetic aperture radar altimeter (SRAL), which allows for inland water levels to be measured with higher precision and improved spatial resolution. However, in regions at middle and high latitudes, where many lakes are covered by ice during the winter, the non-uniformity of the altimeter footprint can substantially impact the accuracy of water level estimates, resulting in abnormal readings when applying standard SRAL synthetic aperture radar (SAR) waveform retracking algorithms (retrackers). In this study, a modified method is proposed to determine the current surface type of lakes, analyzing changes in backscattering coefficients and brightness temperature. This method aligns with ground station observations and ensures consistent surface type classification. Additionally, a dual-threshold algorithm that addresses the limitations of the original bimodal algorithm by identifying multiple peaks without needing elevation correction is introduced. This innovative approach significantly enhances the precision of equivalent water level measurements for ice-covered lakes. The study retrieves and compares the water level data of nine North American lakes covered by ice from 2016–2019 using the dual-threshold and the SAMOSA-3 algorithm with in situ data. For Lake Athabasca, Cedar Lake, Great Slave Lake, Lake Winnipeg, and Lake Erie, the root mean square error (RMSE) of SAMOSA-3 is 39.58 cm, 46.18 cm, 45.75 cm, 42.64 cm, and 6.89 cm, respectively. However, the dual-threshold algorithm achieves an RMSE of 6.75 cm, 9.47 cm, 5.90 cm, 7.67 cm, and 5.01 cm, respectively, representing a decrease of 75%, 79%, 87%, 82%, and 27%, respectively, compared to SAMOSA-3. The dual-threshold algorithm can accurately estimate water levels in ice-covered lakes during winter. It offers a promising prospect for achieving long-term, continuous, and high-precision water level measurements for middle- and high-latitude lakes.

Lake ice phenology (LIP), hiding information about lake energy and material exchange, serves as an important indicator of climate change. Utilizing an efficient technique to swiftly extract lake ice information is crucial in the field of lake ice research. The Bayesian ensemble change detection (BECD) algorithm stands out as a powerful tool, requiring no threshold compared to other algorithms and, instead, utilizing the probability of abrupt changes to detect positions. This method is predominantly employed by automatically extracting change points from time series data, showcasing its efficiency and accuracy, especially in revealing phenological and seasonal characteristics. This paper focuses on Bosten Lake (BL) and employs PMRS data in conjunction with the Bayesian change detection algorithm. It introduces an automated method for extracting LIP information based on the Bayesian change detection algorithm. In this study, the BECD algorithm was employed to extract lake ice phenology information from passive microwave remote sensing data on Bosten Lake. The reliability of the passive microwave remote sensing data was further investigated through cross-validation with MOD10A1 data. Additionally, the Mann–Kendall non-parametric test was applied to analyze the trends in lake ice phenology changes in Bosten Lake. Spatial variations were examined using MOD09GQ data. The results indicate: (1) The Bayesian change detection algorithm (BCDA), in conjunction with PMRS data, offers a high level of accuracy and reliability in extracting the lake ice freezing and thawing processes. It accurately captures the phenological parameters of BL’s ice. (2) The average start date of lake ice freezing is in mid-December, lasting for about three months, and the start date of ice thawing is usually in mid-March. The freezing duration (FD) of lake ice is relatively short, shortening each year, while the thawing speed is faster. The stability of the lake ice complete ice cover duration is poor, averaging 84 days. (3) The dynamic evolution of BL ice is rapid and regionally distinct, with the lake center, southwest, and southeast regions being the earliest areas for ice formation and thawing, while the northwest coastal and Huang Shui Gou areas experience later ice formation. (4) Since 1978, BL’s ice has exhibited noticeable trends: the onset of freezing, the commencement of thawing, complete thawing, and full freezing have progressively advanced in regard to dates. The periods of full ice coverage, ice presence, thawing, and freezing have all shown a tendency toward shorter durations. This study introduces an innovative method for LIP extraction, opening up new prospects for the study of lake ecosystem and strategy formulation, which is worthy of further exploration and application in other lakes and regions.

In ice-covered polar lakes, a narrow ice-free moat opens up in spring or early summer, and then persists at the edge of the lake until complete ice loss or refreezing. In this study, we analyzed the horizontal gradients in Ward Hunt Lake, located in the Canadian High Arctic, and addressed the hypothesis that the transition from its nearshore open-water moat to offshore ice-covered waters is marked by discontinuous shifts in limnological properties. Consistent with this hypothesis, we observed an abrupt increase in below-ice concentrations of chlorophyll a beyond the ice margin, along with a sharp decrease in temperature and light availability and pronounced changes in benthic algal pigments and fatty acids. There were higher concentrations of rotifers and lower concentrations of viruses at the ice-free sampling sites, and contrasts in zooplankton fatty acid profiles that implied a greater importance of benthic phototrophs in their inshore diet. The observed patterns underscore the structuring role of ice cover in polar lakes. These ecosystems do not conform to the traditional definitions of littoral versus pelagic zones but instead may have distinct moat, ice-margin, and ice-covered zones. This zonation is likely to weaken with ongoing climate change.

Further to systematic experiments on the flexural strength of laboratory-grown, fresh water ice loaded cyclically, this paper describes results from new experiments of the same kind on lake ice harvested in Svalbard. The experiments were conducted at −12 °C, 0.1 Hz frequency and outer-fiber stress in the range from ~ 0.1 to ~ 0.7 MPa. The results suggest that the flexural strength increases linearly with stress amplitude, similar to the behavior of laboratory-grown ice.

The Tibetan Plateau (TP) lakes are sensitive to climate change due to its seasonal ice cover, but few studies have paid attention to the freeze-thaw process of TP lakes and its key control parameters. By combining 216 simulation experiments using the LAKE2.0 model with the observations, we evaluated the effects of ice and snow albedo, ice (Kdi) and water (Kdw) extinction coefficients on the lake ice phenology, water temperature, sensible and latent heat fluxes. The reference experiment performs well in simulating the lake temperature, with a small positive bias increasing with depth, but it underestimates the ice thickness. The increase of ice albedo, snow albedo and Kdi induce a significant decrease in water temperature. Compared with the latent heat, the sensible heat flux is more sensitive to these three parameters. The ice thickness increases almost linearly with the increase of ice albedo but decreases with the increase of Kdi. The ice thickness and frozen days vary little with Kdw, but increasing Kdw can decrease the water temperature. Compared with the ice albedo, the Kdi and snow albedo have a large effect on the number of frozen days. This study brings to light the necessity to improve the parameterizations of the TP lakes freeze-thaw process.

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.

Antarctic lakes with perennial ice covers provide the opportunity to investigate in-lake processes without direct atmospheric interaction, and to study their ice-cover sensitivity to climate conditions. In this study, a numerical model – driven by radiative, atmospheric and turbulent heat fluxes from the water body beneath the ice cover – was implemented to investigate the impact of climate change on the ice covers from two Antarctic lakes: west lobe of Lake Bonney (WLB) and Crooked Lake. Model results agreed well with measured ice thicknesses of both lakes (WLB – RMSE= 0.11 m over 16 years of data; Crooked Lake – RMSE= 0.07 m over 1 year of data), and had acceptable results with measured ablation data at WLB (RMSE= 0.28 m over 6 years). The differences between measured and modeled ablation occurred because the model does not consider interannual variability of the ice optical properties and seasonal changes of the lake's thermal structure. Results indicate that projected summer air temperatures will increase the ice-cover annual melting in WLB by 2050, but that the ice cover will remain perennial through the end of this century. Contrarily, at Crooked Lake the ice cover becomes ephemeral most likely due to the increase in air temperatures.

Abstract In most of Environment and Climate Change Canada's (ECCC) current operational systems, inland water physical processes are simulated using a simple water scheme. Water surface temperatures and ice cover fractions are updated daily using analyses. However, ECCC recognizes the need for interactive lakes in its weather and environmental prediction systems, such as those used to forecast surface conditions and floods. As a first step toward this goal, the current study evaluates the impact of the Canadian Small Lake Model (CSLM) in an offline context on surface water temperature, ice phenology and near‐surface atmospheric conditions. The use of CSLM increases lake surface temperatures and decreases its RMSE during ice‐free months, which has a direct impact on the 2‐m air temperature by reducing the cold bias observed in the simulation without CSLM, particularly over larger lakes. CSLM improves ice cover in subgrid lakes, while having a neutral impact on intermediate lakes. On large lakes, CSLM tends to degrade ice cover simulation in southernmost lakes, while improving ice cover in northernmost lakes. The increased lake ice cover in CSLM, particularly over subgrid lakes and in the northern latitudes, has a strong impact on humidity fluxes at the surface during wintertime with a near‐interruption of evapotranspiration over lakes. In summertime, increased water temperature with CSLM leads to a 38% increase in evapotranspiration. With these results, it is expected that the synergy of CSLM and lake‐related observations will improve the simulation and initialization of lake conditions in ECCC's systems.

Seasonal lake ice is sensitive to temperature fluctuations and long-term temperature trends. It is therefore a good indicator of climate warming, which will likely have dramatic impacts on lake ice phenology in northern latitudes. Beside the climate change aspect, lake ice data are important regarding transport and safety issues. In addition, changes in ice cover affect the ecology of the lake and water quality.A new method for accessing lake ice extent (LIE) using optical satellite data was developed at the Finnish Environment Institute. The resulting ICEmod method is based on multidimensional Gaussian distributions calculated for training data using several reflectance and thermal bands and their-related indices. Three alternative classes are provided for a lake ice pixel: (i) open water, (ii) ice cover, and (iii) cloud. The main advantages of ICEmod are the utilization of several spectral bands, inclusion of simultaneous cloud detection, definition of statistical probability for each pixel, the simplicity of processing and easy transition between different satellite sensors. This novel method is applied in the provision of the Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) -based 0.005° Lake Ice Extent product (LIE-NH) for the Northern Hemisphere under the Global Land Service, a component of the Copernicus Land Service.The 0.005° resolution LIE products based on ICEmod applied to SLSTR (an extensive subset corresponding to LIE-NH) were validated against high-resolution Sentinel-2 Multispectral Instrument -images covering lakes in the different parts of the Northern Hemisphere, most of them representing both open water and ice pixels. The gained overall classification accuracy is 96%. The resulting omission error for ice is 6% and omission mainly occurs in lakes where the ice is very dark and fragmented. The ICEmod method is not always able to separate dark ice from open water if there is water on the top of the dark ice, while misclassifications do not occur if the water layer is on white ice. The commission error for ice is 2.5% and is mostly related to the ambiguity in the definition of a ice-pixel: i.e. whether a 0.005° LIE pixel should be classified as ice or open water when the area within the pixel is partially ice-covered. When the lake is completely ice-covered or ice-free, the overall accuracy is 100%. According to the tentative qualitative comparison with European Space Agency Climate Change Initiative Lake Ice Cover (LIC) product, LIE-NH may provide more accurate lake ice information, and it also provides lake ice data for more than 13 000 lakes while LIC operates for 250 lakes only.

As an important part of the cryosphere, lake ice is a sensitive indicator of climate change. Remote sensing technology can quickly and accurately monitor the process of its formation and decay, among which Moderate Resolution Imaging Spectroradiometer (MODIS) images are the most widely used data in the remote sensing monitoring of lake ice. The reasonable selection of monitoring methods is of great significance to grasp the dynamic process and response to climate change of lake ice. In this study, five commonly used remote sensing monitoring methods of lake ice based on MODIS MOD09GA data, including the single band threshold method (SBT), reflectance difference threshold method (RDT), normalized difference snow index method (NDSI), modified normalized difference snow index method (MNDSI) and lake ice index method (LII), were selected to compare their accuracies in extracting lake ice extent by combining them with four evaluation metrics of accuracy, precision, recall and mean intersection over union (MIoU). In addition, the ability of the high-precision LII method for extracting long time series lake ice phenology and its applicability to multiple types of lakes were verified. The results showed that compared with the NDSI method, the other four methods more easily distinguished between lake ice and lake water by setting thresholds. The SBT method and the RDT method had better extraction effects in the freezing process and the melting process, respectively. Compared with the NDSI and MNDSI methods, the LII method showed a significant improvement in lake ice extraction over the entire freeze–thaw cycle, with the smallest mean monitoring error of 1.53% for the percentage of lake ice area in different periods. Meanwhile, the LII method can be used to determine long term lake ice phenology dates and had good performance in extracting lake ice for different types of lakes on the Qinghai–Tibet Plateau with the optimal threshold interval of 0.05~0.07, which can be used for lake ice monitoring and long-term phenological studies in this region.

Landfast ice plays an important role in the nearshore hydrodynamics of large lakes, such as the dampening of surface waves and currents. In this study, previously developed landfast ice basal stress parameterizations were added to an unstructured grid hydrodynamic ice model to represent the effects of grounded ice keels and tensile strength of ice cover. Numerical experiments using this model were conducted to evaluate the development of coastal landfast ice in Lake Superior. A sensitivity study of the free parameters was conducted from December 2018 to May 2021 to cover both high and low ice cover winters in Lake Superior and was compared against observations from the United States National Ice Center. The model reproduces the annual variation in coastal landfast ice in Lake Superior, particularly in shallow nearshore areas and the semi-enclosed bays in the northern regions of the lake. Experiments also show that the growth of landfast ice is mainly controlled by the free parameter that controls the critical ice thickness for the activation of basal stress. Overall, the model tends to underestimate the extent of coastal landfast against observations.

Arctic lakes are sensitive to climate change, and the timing and duration of ice presence and absence (i.e., ice phenology) on the lake surface can be used as a climate indicator. In this study of Linnévatnet, one of the largest lakes on Svalbard, we compare inferences of lake ice duration from satellite data with continuously monitored lake water temperature and photographs from automatic cameras. Visible surface reflectance data from the moderate-resolution imaging spectroradiometer (MODIS) were used to observe the change in the lake-wide mean surface reflectance of Linnévatnet from 2003–2019, and smoothing splines were applied to the to determine the date of summer ice-off (also called “break-up end”—BUE). Similarly, BUE and fall ice-on (or “freeze-up end”—FUE) were determined from lake-wide mean time series of Sentinel-1 microwave backscatter from 2014–2019. Overall, the ice timing dates identified from the satellite observations agree well with the in-situ observations (RMSE values of approximately 2–7 days for BUE and FUE, depending on the method and in-situ dataset), lending confidence to the accuracy of remote sensing of lake ice phenology in remote Arctic regions. Our observations of Linnévatnet indicate that BUE dates do not have a significant trend, while FUE dates have been occurring approximately 1.5 days later per year during the study period. These results support an overall decrease in annual duration of lake ice cover in this part of Svalbard.

Autonomous temperature data loggers were used to measure the temperature profile within a growing ice cover and in the water below. The ice formed under natural conditions over the pond. We observed the presence of distinct layers of gas bubbles throughout the ice thickness. Temperature measurements allowed us to determine growth rates (μm s−1) and cooling rates (°C s−1) of the ice and demonstrated that these bubble layers formed during the peak ice growth rates from 0.58 to 0.92 µm s−1. The growth rates, leading to the formation of layers of bubbles, were more than an order of magnitude lower than for bubbles produced in controlled laboratory conditions (from 3 to 80 µm s−1). This observation introduces the possibility that solid impurities play a role in natural waters and that they must lower the limit of growth rates required for bubble occurrence. Data revealed a decrease in ice growth rates while cooling rates increased. We interpret this observation as an effect of the heat flux from the water to the ice (8.34–34.11 W m−2), and of gas concentration changes in the water below. Calculations of the ice thickness using traditional methods showed the necessity to include the heat flux from the water to the ice and the effect of gas bubbles within the ice and near the ice–water interface.

This article is an attempt to analyse and compare several selected parameters regarding ice phenomena using the correlation analysis of two lakes, which are benchmark lakes located in the central part of the Kashubian Lakeland. These lakes are: Raduńskie Górne, a larger one (387.2 hm2) and Ostrzyckie, a smaller one covering an area of 308.0 hm2. The analysis covered measurement sequences for the period 1971–2010. The material for analysis regarding data on ice phenomena on Lake Raduńskie Górne was obtained from the University of Gdańsk Limnological Station in Borucino while the data for Lake Ostrzyckie was obtained from the Institute of Meteorology and Water Management (IMGW). Relations between the following parameters of the ice regime were analysed: duration of the ice phenomena, duration of the ice cover, average and maximum thickness of the ice cover. The analysis and data comparisons have revealed that there are strong and very strong relations between the analysed parameters, which made it possible to develop proper statistical models. Hence, should there be a lack of data on the ice-cover pattern for one of these lakes it is possible to recreate them using the elaborated empirical models and data for the other.

The Tibetan Plateau (TP) lakes are sensitive to climate change due to ice-albedo feedback, but almost no study has paid attention to the ice albedo of TP lakes and its potential impacts. Here we present a recent field experiment for observing the lake ice albedo in the TP, and evaluate the applicability of the Moderate Resolution Imaging Spectroradiometer (MODIS) products as well as ice-albedo parameterizations. Most of the observed lake ice albedos on TP are

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° 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°, but also reflect the U-shaped distribution of the diurnal albedo as the solar altitude angle exceeds 15°. 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.

Lake ice phenology is a climate-sensitive indicator. However, ground-based monitoring suffers from the limitations of human vision and the difficulty of its implementation in harsh environments. Remote sensing provides great potential to detect lake ice phenology. In this study, a new automated method was developed to extract lake ice phenology parameters by capturing the temporal pattern of the transitional water/ice phase using a parameterized time function. The method is based on Moderate-Resolution Imaging Spectroradiometer (MODIS) daily temperature products, which have unique potential for monitoring lake ice cover as a result of providing four observations per day at 1 km spatial resolution from 2002 to 2016. Three seasonally ice-covered lakes with different characteristics in different climate regions were selected to test the method during the period of 2002–2016. The temporal pattern of water/ice transition phase was determined on the basis of unfrozen water cover fraction extracted from the MODIS daily temperature data, and was compared with the MODIS snow and reflectance products and Landsat images. A good agreement with an R2 of above 0.8 was found when compared with the MODIS snow product. The annual variation of extracted ice phenology dates showed good consistency with the MODIS reflectance and AMSR-E/2 products. The approach was then applied to nine seasonally ice-covered lakes in northern China from 2002 to 2016. The strongest tendency towards a later freeze-up start date was revealed in Lake Qinghai (6.31 days/10 yr) among the lakes in Tibetan plateau, and the break-up start and end dates rapidly shifted towards earlier dates in Lake Hulun (−3.73 days/10 yr; −5.02 days/10 yr). The method is suitable for estimating and monitoring ice phenology on different types of lakes over large scales and has a strong potential to provide valuable information on the responses of ice processes to climate change.

The aim of this study was to determine the dynamics of the process of a course of ice creation phenomena in two small water bodies located in the Silesian Upland. The studies and observations of ice formation on the water bodies were conducted during the period 10th November 2011 to 23rd March 2012. The following parameters were determined each day: degree of ice coverage on each water body, thickness and ice structure and thickness of snow cover on each water body. From the studies it results that a course of the ice formation of both water bodies was almost identical. The same maximum ice thickness was recorded in both cases. It was 36 cm in that season, with slight differences in average thickness. The course of particular phases of ice formation in different water regions was also very similar. The number of days with the ice phenomena and the number of days from the beginning to the end of the ice phenomena were identical in both cases, being 96 and 131 days, respectively. The slight differences over several days were recorded in the case of: number of days with shore ice (lb), number of days with partial ice cover (lcz), number of days with an incomplete ice cover (lnp), number of breaks in the ice cover (B). Additionally, with daily measurements of ice cover thickness the relationships between the course of the average daily air temperature from the meteorological station of Faculty of Earth Sciences of University of Silesia and the daily changes in the ice thickness in the water regions in question were determined by using Spearman’s correlation coefficient. In both cases the relationships were strong and they were r= −0,84(p

To gain more understanding of lake ice melting process, field research was carried out in an arctic tundra lake, Kilpisjärvi (surface area 37.1 km2, maximum depth 57 m) in the melting periods of 2013 and 2014. The heat budget of the ice cover was dominated by the radiation balance; turbulent heat fluxes were large in 2013 due to warm air advection but small in 2014. Transmittance of solar radiation through ice was 0.25 in 2013 and 0.10 in 2014, snow-ice was absent in 2013 but in 2014 accounted for 50% of the ice cover. The melting rate was 4.4 cm d-1 in 2013, 1.9 cm d-1 in 2014. The portions of surface, bottom and internal melting were, respectively, 2.9, 1.0 and 0.5 cm d-1 in 2013 and 0.8, 1.0 and 0.1 cm d-1 in 2014. Internal melting was realized in increase of ice porosity. In 2013 a rapid ice breakage event completed the ice breakup in short time when ice porosity had reached 40-50%. A lake ice melting model should include the thickness and porosity of ice, with porosity connected to an ice strength criterion.

Seepage of geological methane through sediments of Arctic lakes might contribute conceivably to the atmospheric methane budget. However, the abundance and precise locations of such seeps are poorly quantified. For Lake Neyto, one of the largest lakes on the Yamal Peninsula in Northwestern Siberia, temporally expanding regions of anomalously low backscatter in C-band SAR imagery acquired in late winter and spring have been suggested to be related to seepage of methane from hydrocarbon reservoirs. However, this hypothesis has not been verified using in-situ observations so far. Similar anomalies have also been identified for other lakes on Yamal, but it is still uncertain whether or how many of them are related to methane seepage. This study aimed to document similar lake ice backscatter anomalies on a regional scale over four study regions (the Yamal Peninsula and Tazovskiy Peninsulas; the Lena Delta in Russia; the National Petroleum Reserve Alaska) during different years using a time series based approach on Google Earth Engine (GEE) that quantifies changes of σ0 from the Sentinel-1 C-band SAR sensor over time. An algorithm for assessing the coverage that takes the number of acquisitions and maximum time between acquisitions into account is presented, and differences between the main operating modes of Sentinel-1 are evaluated. Results show that better coverage can be achieved in extra wide swath (EW) mode, but interferometric wide swath (IW) mode data could be useful for smaller study areas and to substantiate EW results. A classification of anomalies on Lake Neyto from EW Δσ0 images derived from GEE showed good agreement with the classification presented in a previous study. Automatic threshold-based per-lake counting of years where anomalies occurred was tested, but a number of issues related to this approach were identified. For example, effects of late grounding of the ice and anomalies potentially related to methane emissions could not be separated efficiently. Visualizations of Δσ0 images likely reflect the temporal expansions of anomalies and are expected to be particularly useful for identifying target areas for future field-based research. Characteristic anomalies that clearly resemble the ones observed for Lake Neyto could be identified solely visually in the Yamal and Tazovskiy study regions. All data and algorithms produced in the framework of this study are openly provided to the scientific community for future studies and might potentially aid our understanding of geological lake seepage upon the progression of related field-based studies and corresponding evaluations of formation hypotheses.

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.

A 1-D ice cover model was developed to predict and constrain drivers of long-term ice thickness trends in chemically stratified lakes of Taylor Valley, Antarctica. The model is driven by surface radiative heat fluxes and heat fluxes from the underlying water column. The model successfully reproduced 16 a (between 1996 and 2012) of ice thickness changes for the west lobe of Lake Bonney (average ice thickness = 3.53 m) and Lake Fryxell (average ice thickness = 4.22 m). Long-term ice thickness trends require coupling with the thermal structure of the water column. The heat stored within the temperature maximum of lakes exceeding a liquid water column depth of 20 m can either impede or facilitate ice thickness change depending on the predominant climatic trend (cooling or warming). As such, shallow (

Lake-ice properties at Lake Abashiri, Hokkaido, Japan, were examined using field observations and a 1-D thermodynamic model to clarify formation processes at mid-latitudes subject to significant snowfall as well as moderate air temperature. At all lake sites examined, the ice comprised two distinct layers: a snow ice (SI) layer on top and a congelation ice (CI) layer below. The SI layer occupied as much as 29–73% of the total ice thickness, a much greater fraction than that reported for lakes at Arctic high latitudes. In the model, the CI growth rate was estimated using the traditional heat budget method, while the SI growth rate was calculated assuming the excessive snowfall from the isostatic balance is converted to SI by a snow compression rate (β) with the surface melting rate added when the surface heat budget becomes positive. By tuning the value of β to the observational results of SI thickness, the model outcome successfully reproduced the observational thicknesses of CI and SI, and the break-up date of the lake. Essentially, the model findings show how snow and its formation into SI reduce, by about half, the seasonal variability of total ice thickness.

The Qinghai-Tibetan Plateau (QTP) is characterized by a cold climate and a large number of lakes. The long ice season necessitates study of the widespread ice covers in the region. An unprecedented multidisciplinary field campaign was conducted on lake ice processes in the central QTP during the period 2019–13. The study lake generally froze up in late October or early November, and broke up in mid or late April, with a maximum ice thickness of 50–70 cm. The mass balances at both ice surface and bottom were measured continuously. Significant ice surface sublimation/ablation was detected and accounted for up to 40% of the whole ice thickness over the ice season. A simple heat-transfer model was developed for the surface ice loss. The calculated values were in good agreement with the observations. They also indicated that atmospheric conditions, including low air humidity and prevailing strong winds, are the primary drivers of the ice surface sublimation.

Changes to ice cover on lakes throughout the northern landscape has been established as an indicator of climate change and variability, expected to have implications for both human and environmental systems. Monitoring lake ice cover is also required to enable more reliable weather forecasting across lake-rich northern latitudes. Currently, the Canadian Ice Service (CIS) monitors lakes using synthetic aperture radar (SAR) and optical imagery through visual interpretation, with total lake ice cover reported weekly as a fraction out of ten. An automated method of classification would allow for more detailed records to be delivered operationally. In this research, we present an automatic ice-mapping approach which integrates unsupervised segmentation from the Iterative Region Growing using Semantics (IRGS) algorithm with supervised random forest (RF) labeling. IRGS first locally segments homogeneous regions in an image, then merges similar regions into classes across the entire scene. Recently, these output regions were manually labeled by the user to generate ice maps, or were labeled using a Support Vector Machine (SVM) classifier. Here, three labeling methods (Manual, SVM, and RF) are applied after IRGS segmentation to perform ice-water classification on 36 RADARSAT-2 scenes of Great Bear Lake (Canada). SVM and RF classifiers are also tested without integration with IRGS. An accuracy assessment has been performed on the results, comparing outcomes with author-generated reference data, as well as the reported ice fraction from CIS. The IRGS-RF average classification accuracy for this dataset is 95.8%, demonstrating the potential of this automated method to provide detailed and reliable lake ice cover information operationally.

Lake ice is a dominant component of Canada’s landscape and can act as an indicator for how freshwater aquatic ecosystems are changing with warming climates. While lake ice monitoring through government networks has decreased in the last three decades, the increased availability of remote sensing images can help to provide consistent spatial and temporal coverage for areas with annual ice cover. Synthetic aperture radar (SAR) data are commonly used for lake ice monitoring, due to the acquisition of images in any condition (time of day or weather). Using Sentinel-1 A/B images, a high-density time series of SAR images was developed for Lake Hazen in Nunavut, Canada, from 2015−2018. These images were used to test two different methods of monitoring lake ice phenology: one method using the first difference between SAR images and another that applies the Otsu segmentation method. Ice phenology dates determined from the two methods were compared with visual interpretation of the Sentinel-1 images. Mean errors for the pixel comparison of the first difference method ranged 3−10 days for ice-on and ice-off, while average error values for the Otsu method ranged 2−10 days. Mean errors for comparisons of different sections of the lake ranged 0−15 days for the first difference method and 2−17 days for the Otsu method. This research demonstrates the value of temporally consistent image acquisition for improving the accuracy of lake ice monitoring.

At the end of January 2012, a low-level cloud from partly ice-free Lake Ladoga caused very variable 2-m temperatures in Eastern Finland. The sensitivity of the High Resolution Limited Area Model (HIRLAM) to the lake surface conditions was tested in this winter anticyclonic situation. The lake appeared to be (incorrectly) totally covered by ice when the lake surface was described with its climatology. Both parametrisation of the lake surface state by using a lake model integrated to the NWP system and objective analysis based on satellite observations independently resulted in a correct description of the partly ice-free Lake Ladoga. In these cases, HIRLAM model forecasts were able to predict cloud formation and its movement as well as 2-m temperature variations in a realistic way. Three main conclusions were drawn. First, HIRLAM could predict the effect of Lake Ladoga on local weather, when the lake surface state was known. Second, the current parametrisation methods of air–surface interactions led to a reliable result in conditions where the different physical processes (local surface processes, radiation and turbulence) were not strong, but their combined effect was important. Third, these results encourage work for a better description of the lake surface state in NWP models by fully utilising satellite observations, combined with advanced lake parametrisation and data assimilation methods.

Remote sensing of ice phenology for small lakes is hindered by a lack of satellite observations with both high temporal and spatial resolutions. By merging multi-source satellite data over individual lakes, we present a new algorithm that successfully estimates ice freeze and thaw timing for lakes with surface areas as small as 0.13 km2 and obtains consistent results across a range of lake sizes. We have developed an approach for classifying ice pixels based on the red reflectance band of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, with a threshold calibrated against ice fraction from Landsat Fmask over each lake. Using a filter derived from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) surface air temperature product, we removed outliers in the time series of lake ice fraction. The time series of lake ice fraction was then applied to identify lake ice breakup and freezeup dates. Validation results from over 296 lakes in Maine indicate that the satellite-based lake ice timing detection algorithm perform well, with mean absolute error (MAE) of 5.54 days for breakup dates and 7.31 days for freezeup dates. This algorithm can be applied to lakes worldwide, including the nearly two million lakes with surface area between 0.1 and 1 km2.

Thermokarst lakes in the Arctic and Subarctic release carbon from thawing permafrost in the form of methane and carbon dioxide with important implications for regional and global carbon cycles. Lake ice impedes the release of gas during the winter. For instance, bubbles released from lake sediments become trapped in downward growing lake ice, resulting in vertically-oriented bubble columns in the ice that are visible on the lake surface. We here describe a classification technique using an object-based image analysis (OBIA) framework to successfully map ebullition bubbles in airborne imagery of early winter ice on an interior Alaska thermokarst lake. Ebullition bubbles appear as white patches in high-resolution optical remote sensing images of snow-free lake ice acquired in early winter and, thus, can be mapped across whole lake areas. We used high-resolution (9–11 cm) aerial images acquired two and four days following freeze-up in the years 2011 and 2012, respectively. The design of multiresolution segmentation and region-specific classification rulesets allowed the identification of bubble features and separation from other confounding factors such as snow, submerged and floating vegetation, shadows, and open water. The OBIA technique had an accuracy of >95% for mapping ebullition bubble patches in early winter lake ice. Overall, we mapped 1195 and 1860 ebullition bubble patches in the 2011 and 2012 images, respectively. The percent surface area of lake ice covered with ebullition bubble patches for 2011 was 2.14% and for 2012 was 2.67%, representing a conservative whole lake estimate of bubble patches compared to ground surveys usually conducted on thicker ice 10 or more days after freeze-up. Our findings suggest that the information derived from high-resolution optical images of lake ice can supplement spatially limited field sampling methods to better estimate methane flux from individual lakes. The method can also be used to improve estimates of methane ebullition from numerous lakes within larger regions.

We present results from the coupling of FLake to the Met Office Unified Model (MetUM). The coupling and initialisation are first described, and the results of testing the coupled model in local and global model configurations are presented. These show that FLake has a small statistical impact on screen temperature, but has the potential to modify the weather in the vicinity of areas of significant inland water. Examination of FLake lake ice has revealed that the behaviour of lakes in the coupled model is unrealistic in some areas of significant sub-grid orography. Tests of various modifications to ameliorate this behaviour are presented. The results indicate which of the possible model changes best improve the annual cycle of lake ice. As FLake has been developed and tuned entirely outside the Unified Model system, these results can be interpreted as a useful objective measure of the performance of the Unified Model in terms of its near-surface characteristics.

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.

Long-term, integrated records of limnology are rare in the central United States. Mozingo Lake is a reservoir in northwestern Missouri that was sampled regularly since its creation in 1994. Physical data were collected during 121 visits and compared to meteorological observations. July hypolimnetic temperatures have risen rapidly (2.4°C / decade: P=0.037), suggesting weakened summer stratification in the future. Winter conditions were rarely correlated with lake conditions in the following July; the exception is July epilimnion temperature, which correlated with ice-over date, January hypolimnion temperature, and ice duration (P=0.006, 0.010, and 0.024). In contrast, winter ice-over date was best correlated with air temperature in the preceding July (P=0.006); other factors were not significantly correlated, including fall air temperatures, July epilimnion temperatures, and October water column temperatures. Analysis of air temperatures preceding ice-over revealed that the strongest correlation was with a 68-day average air temperature of 4.8°C. July air temperatures, along with ice-over date, correlated with January ice thickness and ice duration (P=0.014 and 0.001, respectively). This suggests that a warm July is associated with a mild winter, a relationship confirmed by a significant correlation (P=0.011). Ice thickness, ice duration, and ice-out date also correlated with winter air temperature (P≤0.003 for each). It therefore appears that summer conditions influence winter conditions which, in turn, influence certain lake conditions in the following summer; this observation indicates that winter conditions may not reset physical parameters in lakes. Legacies thus may span various intervals, ranging from a week to a year or more. Although the Mozingo Lake record is brief (20 years), it suggests directions for longer-term studies. Multi-year legacy effects have rarely been documented, but in Mozingo Lake they suggest that a single strong climatic anomaly may affect the lake for several years.

Lake surface temperature (LST) and ice phenology were simulated for various points differing in depth on Great Slave Lake and Great Bear Lake, two large lakes located in the Mackenzie River Basin in Canada's Northwest Territories, using the 1-D Freshwater Lake model (FLake) and the Canadian Lake Ice Model (CLIMo) over the 2002–2010 period, forced with data from three weather stations (Yellowknife, Hay River and Deline). LST model results were compared to those derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observing System Terra and Aqua satellite platforms. Simulated ice thickness and freeze-up/break-up dates were also compared to in situ observations. Both models showed a good agreement with daily average MODIS LSTs on an annual basis (0.935 ≤ relative index of agreement ≤ 0.984 and 0.94 ≤ mean bias error ≤ 4.83). The absence of consideration of snow on lake ice in FLake was found to have a large impact on estimated ice thicknesses (25 cm thicker on average by the end of winter compared to in situ measurements; 9 cm thicker for CLIMo) and break-up dates (6 d earlier in comparison with in situ measurements; 3 d later for CLIMo). The overall agreement between the two models and MODIS LST products during both the open water and ice seasons was good. Remotely sensed data are a promising data source for assimilation into numerical weather prediction models, as they provide the spatial coverage that is not captured by in situ data.

A field programme on light conditions in ice-covered lakes and optical properties of lake ice was performed in seven lakes of Finland and Estonia in February–April 2009. On the basis of irradiance measurements above and below ice, spectral reflectance and transmittance were determined for the ice sheet; time evolution of photosynthetically active radiation (PAR) transmittance was examined from irradiance recordings at several levels inside the ice sheet. Snow cover was the dominant factor for transmission of PAR into the lake water body. Reflectance was 0.74–0.92 in winter, going down to 0.18–0.22 in the melting season. The bulk attenuation coefficient of dry snow was 14–25 m–1; the level decreased as the spring was coming. The reflectance and bulk attenuation coefficient of snow-free ice were 0.1–0.4 and 1–5 m–1. Both were considerably smaller than those of snow cover. Seasonal evolution of light transmission was mainly due to snow melting. Snow and ice cover not only depress the PAR level in a lake but also influence the spectral and directional distribution of light.

Lake ice is a significant component of the cryosphere due to its large spatial coverage in high-latitude regions during the winter months. The Laurentian Great Lakes are the world’s largest supply of freshwater and their ice cover has a major impact on regional weather and climate, ship navigation, and public safety. Ice experts at the Canadian Ice Service (CIS) have been manually producing operational Great Lakes image analysis charts based on visual interpretation of the synthetic aperture radar (SAR) images. In that regard, we have investigated the performance of the semi-automated segmentation algorithm “glocal„ Iterative Region Growing with Semantics (IRGS) for lake ice classification using dual polarized RADARSAT-2 imagery acquired over Lake Erie. Analysis of various case studies indicated that the “glocal„ IRGS algorithm could provide a reliable ice-water classification using dual polarized images with a high overall accuracy of 90.4%. However, lake ice types that are based on stage of development were not effectively identified due to the ambiguous relation between backscatter and ice types. The slight improvement of using dual-pol as opposed to single-pol images for ice-water discrimination was also demonstrated.