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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.

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. [ABSTRACT FROM AUTHOR]

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 <0.12, and the clear blue ice albedo is only 0.075, much lower than reported in the previous studies. Even that of ice covered with snow patches is only 0.212. MOD10A1 albedo product has the best agreement with observations, followed by those of MYD10A1. MCD43A3 product is consistently higher than the observations. Due to an error of snow flag and inconsistent time windows in MCD43A2 and MCD43A3, at certain times, the albedo of the ice without snow is even higher than that covered with snow. When the solar zenith angle is not considered, there is no significant correlation between the albedo and the ice surface temperature. None of the existing ice-albedo parameterizations can reproduce well the observed relationship of the albedo and surface temperature. [ABSTRACT FROM AUTHOR]

Monitoring and analyzing the (decreasing) trends in lake freezing provides important information for climate research. Multi-temporal satellite images are a natural data source to survey ice on lakes. In this paper, we describe a method for lake ice monitoring, which uses low spatial resolution (250m -1000m) satellite images to determine whether a lake is frozen or not. We report results on four selected lakes in Switzerland: Sihl, Sils, Silvaplana and St. Moritz. These lakes have different properties regarding area, altitude, surrounding topography and freezing frequency, describing cases of medium to high difficulty. Digitized Open Street Map (OSM) lake outlines are back-projected on to the image space after generalization. As a pre-processing step, the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images. We define the lake ice detection as a two-class (frozen, non-frozen) semantic segmentation problem. Several spectral channels of the multi-spectral satellite data are used, both reflective and emissive (thermal). Only the cloud-free (clean) pixels which lie completely inside the lake are analyzed. The most useful channels to solve the problem are selected with xgboost and visual analysis of histograms of reference data, while the classification is done with non-linear support vector machine (SVM). We show experimentally that this straight-forward approach works well with both MODIS and VIIRS satellite imagery. Moreover, we show that the algorithm produces consistent results when tested on data from multiple winters. [ABSTRACT FROM AUTHOR]

A winter time series of ground-based (X- and Ku-bands) scatterometer and spaceborne synthetic aperture radar (SAR) (C-band) fully polarimetric observations coincident with in situ snow and ice measurements are used to identify the dominant scattering mechanism in bubbled freshwater lake ice in the Hudson Bay Lowlands near Churchill, Manitoba. Scatterometer observations identify two physical sources of backscatter from the ice cover: the snow–ice and ice–water interfaces. Backscatter time series at all frequencies show increases from the ice–water interface prior to the inclusion of tubular bubbles in the ice column based on in situ observations, indicating scattering mechanisms independent of double-bounce scatter. The co-polarized phase difference of interactions at the ice–water interface from both scatterometer and SAR observations is centered at 0° during the time series, also indicating a scattering regime other than double bounce. A Yamaguchi three-component decomposition of the RADARSAT-2 C-band time series is presented, which suggests the dominant scattering mechanism to be single-bounce off the ice–water interface with appreciable surface roughness or preferentially oriented facets, regardless of the presence, absence, or density of tubular bubble inclusions. This paper builds on newly established evidence of single-bounce scattering mechanism for freshwater lake ice and is the first to present a winter time series of ground-based and spaceborne fully polarimetric active microwave observations with polarimetric decompositions for bubbled freshwater lake ice. [ABSTRACT FROM AUTHOR]

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

During the winter of 2014, a weak polar vortex brought record cold temperatures to the north-central (“Midwest”) United States, and the Great Lakes reached the highest extent of ice coverage (92.5%) since 1979. This event shut down the generation of seismic signals caused by wind-driven wave action within the lakes (termed “lake microseisms”), giving an unprecedented opportunity to isolate and characterize these novel signals through comparison with nonfrozen time periods. Using seismic records at 72 broadband stations, we observe Great Lakes microseism signals at distances >300 km from the lakes. In contrast to conventional oceanic microseisms, there is no clear relationship between the frequency content of the seismic signals (observed from ~0.5–5-s period) and the dominant swell period or resonance periods of the lakes based on their bathymetric profiles. Thus, the exact generation mechanism is not readily explained by conventional microseism theory and warrants further investigation. [ABSTRACT FROM AUTHOR]

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.

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

Abstract: Detailed observations of thermal structure over an entire winter in a large lake reveal the presence of large (10–20 m) overturns under the ice, driven by diurnal solar heating. Convection can occur in the early winter, but the most vigorous convection occurred near the end of winter. Both periods are when our lake ice model suggest thinner ice that would have been transparent. This under‐ice convection led to a deepening of the mixed layer over time, consistent with previous short‐term studies. During periods of vigorous convection under the ice at the end of winter, the dissolved oxygen had become supersaturated from the surface to 23 m below the surface, suggesting abundant algal growth. Analysis of our high‐frequency observations over the entire winter of 2015 using the Thorpe‐scale method quantified the scale of mixing. Furthermore, it revealed that changes in oxygen concentrations are closely related to the intensity of mixing. [ABSTRACT FROM AUTHOR]

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 (s0) 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. [ABSTRACT FROM AUTHOR]

Lakes are a prominent geographic feature in northern landscapes and play an important role in understanding regional climate systems. In order to better model changes within climate systems, it is important to study lake ice processes. Although the availability of records for lake ice through ground measurements has declined in recent years, the increased use of remote sensing provides an alternative to this. Using a preclassified snow and ice remote sensing product with a 500-m resolution, based on images from the Moderate Resolution Imaging Spectroradiometer (MODIS/MOD10A1), and the use of measured and reanalysis temperature data, this study evaluated lake ice phenology dates in connection to recent trends in temperature and 0 °C isotherms within Ontario and Manitoba between 2001 and 2014. Temperature trends indicated both regional warming and cooling, with significant cooling observed in Southern Ontario ( p < .05) and significant warming in Southern Manitoba ( p < .1) during the fall. Spatial analysis of the trends in the lake ice data showed significant clustering of significant trends in ice on dates ( p < .01). When analysing the trends in ice phenology in connection to the trends in temperature, it was found that 70% of lakes experienced a change in the ice on date with the expected change in temperature and 85% of lakes for ice off date. When shifting ice on and ice off dates are investigated in relation to 0 °C isotherms, it was seen that 80% of ice on dates and 100% of ice off dates shifted in sync with the isotherm dates. This demonstrates that the ice phenology of lakes in Ontario and Manitoba, Canada, is responding to short-term variability in temperature. The MODIS product could be used to investigate ice phenology on a large scale and contribute towards expanding existing records of ice phenology. Establishing long-term ice records could be a valuable asset for other research ranging from water balance studies to the response of lake biota under changing climate. [ABSTRACT FROM AUTHOR]

Observations of ice thickness are limited in high latitude regions, at a time when they are increasingly being requested by operational ice centers. This study aims to improve the retrieval of lake ice thickness using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA’s Aqua (P.M.) and Terra (A.M.) satellites. The accuracy of ice thickness retrievals based on MODIS lake ice surface temperature (LIST) is investigated using a commonly used heat balance equation and the retrieved ice thicknesses are compared to in situ measurements from the Canadian Ice Service. The accuracy of ice thickness estimates is improved when using snow depth from the 1-D thermodynamic lake ice model Canadian Lake Ice Model (CLIMo) rather than an empirical relationship between snow depth and ice thickness utilized in the recent investigations. Taking into account all data over the study period (2002–2014), the mean bias error and the root-mean-square error are reduced from −0.42 to 0.07 m and 0.58 to 0.17 m, respectively, with the novel approach proposed herein. However, this approach is limited to ice thickness estimations of less than ca. 1.7 m. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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

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.

Lake ice is an important component in understanding the local climate as changes in temperature have an impact on the timing of key ice phenology events. In recent years, there has been a decline in the in-situ monitoring of lake ice events in Canada and microwave remote sensing imagery from synthetic aperture radar (SAR) is more widely used due to the high spatial resolution and response of backscatter to the freezing and melting of the ice surface. RADARSAT-2 imagery was used to develop a threshold-based method for determining lake ice events for mid-latitude lakes in Central Ontario from 2008 to 2017. Estimated lake ice phenology events are validated with ground-based observations and are compared against the Moderate Resolution Imaging Spectroradiometer (MODIS band 2). The threshold-based method was found to accurately identify 12 out of 17 freeze events and 13 out of 17 melt events from 2015–2017 when compared to ground-based observations. Mean absolute errors for freeze events ranged from 2.5 to 10.0 days when compared to MODIS imagery while the mean absolute error for water clear of ice (WCI) ranged from 1.5 to 7.1 days. The method is important for the study of mid-latitude lake ice due to its unique success in detecting multiple freeze and melting events throughout the ice season.