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Temperate lakes worldwide are losing ice cover but the implications for under‐ice thermal dynamics are poorly constrained. Using a 92‐year record of ice phenology from a temperate and historically dimictic lake, we examined trends, variability, and drivers of ice phenology and under‐ice temperatures. The onset of ice formation decreased by 23 days century−1, which can be largely attributed to warming air temperatures. Ice‐off date has become substantially more variable with spring air temperatures and cumulative February through April snowfall explaining over 80% of the variation in timing. As a result of changing ice phenology, total ice duration contracted by a month and more than doubled in interannual variability. Using weekly under‐ice temperature profiles for the most recent 36 years, we found that shorter ice duration decreased winter inverse stratification and was associated with an extended spring mixing period. We illustrate the limitations of relying on discrete ice clearance dates in our assumptions around under‐ice thermal dynamics by presenting high‐frequency under‐ice observations in two recent winters: one with intermittent ice cover and a year with slow spring ice clearance. Plain Language Summary: Lakes worldwide are losing ice cover in response to climate change. We used a rare and nearly century‐long data set of ice formation and ice clearance records to examine trends, variability, and drivers. We found that ice cover is getting substantially shorter and more variable with winter ice duration about a month shorter now than it was a century ago. Using under‐ice temperature measurements from the most recent three decades, we found differences in under‐ice temperatures affected by lake ice duration. Key Points: Ice duration and ice formation are getting shorter or occurring later, and both are increasing in interannual variabilityIce break up is not trending earlier, but interannual variability is increasingThis dimictic lake is at risk of transitioning toward monomixis in the near future [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

Real‐time forecasted ice information for large lakes, such as the Great Lakes, is critical for essential operations, such as ice breaking, commercial navigation, search and rescue, and oil spill response. Existing forecast products for large lake ice conditions are not available for medium‐range time horizons (5–16 days out), yet they could provide important information for decision making, particularly for ice breaking and spill responses. In addition, ice forecasts for Earth's largest lakes at these timescales could be important for Medium‐Range Weather (MRW) forecasting. However, the skill of existing operational products in predicting ice conditions at MRW timescales has not been studied. This work aims to determine how well ice forecasts from a coupled large lake hydrodynamic‐ice model perform for MRW forecast horizons. Simulations were carried out for the 2022 Great Lakes ice season, using 8 different 16‐day forecast periods. Forecast results were compared to observations of meteorology and ice conditions from the U.S. National Ice Center. Results show the MRW ice forecasts in the Great Lakes outperform persistence‐based forecasts. These findings could inform the development or extension of lake operational ice forecasting and the potential of coupling between atmospheric and large lake models at medium‐range forecast time scales. Plain Language Summary: Ice information for large lakes is critical for essential operations, such as ice breaking, commercial navigation, search and rescue, and oil spill response. Ice forecasts are currently not available in the medium‐range (5–16 days out) time scale, yet they could provide valuable information for decision making. This information could also be important for medium‐range weather forecasts. The skill of existing Great Lakes ice forecast products at medium‐range time scales is currently unknown. Our work aims to determine how well ice forecasts from an atmosphere‐lake model perform for medium‐range time scales. Simulations were carried out for the 2022 Great Lakes ice season, using 8 different 16‐day forecast periods. The modeled forecasts were compared to observations from the National Ice Center and observed meteorological conditions. Results show that the modeled forecasts outperform persistence forecasts. These findings could inform the development or extension of ice forecasting and the potential of an atmosphere‐lake model at medium‐range time scales. Key Points: Medium‐range (5‐ to 16‐day) ice forecasts show utility in the Great LakesModel ice forecasts outperform ice persistence forecastsAir temperature bias in medium‐range weather forecasts is the likely greatest contributor to ice error [ABSTRACT FROM AUTHOR]

Lakes in Southwest Alaska are a critical habitat to many species and provide livelihoods to many communities through subsistence fishing, transportation, and recreation. Consistent and reliable data are rarely available for even the largest lakes in this sparsely populated region, so data-intensive methods utilizing long-term observations and physical data are not possible. To address this, we used optical remote sensing (MODIS 2002–2016) to establish a phenology record for key lakes in the region, and we modeled lake-ice formation and breakup for the years 1982–2022 using readily available temperature and solar radiation-based predictors in a survival modeling framework that accounted for years when lakes did not freeze. Results were validated with observations recorded at two lakes, and stratification measured by temperature arrays in three others. Our model provided good predictions (mean absolute error, freeze-over = 11 days, breakup = 16 days). Cumulative freeze-degree days and cumulative thaw-degree days were the strongest predictors of freeze-over and breakup, respectively. Lake volume appeared to mediate lake-ice phenology, as ice-cover duration tended to be longer and less variable in lower-volume lakes. Furthermore, most lakes < 10 km3 showed a trend toward shorter ice seasons of −1 to −6 days/decade, while most higher-volume lakes showed undiscernible or positive trends of up to 2 days/decade. Lakes > 20 km3 also showed a greater number of years when freeze-over was neither predicted by our model (37 times, n = 200) nor observed in the MODIS record (19 times, n = 60). While three lakes in our study did not commonly freeze throughout our study period, four additional high-volume lakes began experiencing years in which they did not freeze, starting in the late 1990s. Our study provides a novel approach to lake-ice prediction and an insight into the future of lake ice in the Boreal region. [ABSTRACT FROM AUTHOR]

Lake ice thickness (LIT) is a sensitive indicator of climate change, identified as a thematic variable of Lakes as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Here, we present a novel and efficient analytically based retracking approach for estimating LIT from high-resolution Ku-band (13.6 GHz) synthetic-aperture radar (SAR) altimetry data. The retracker method is based on the analytical modeling of the SAR radar echoes over ice-covered lakes that show a characteristic double-peak feature attributed to the reflection of the Ku-band radar waves at the snow–ice and ice–water interfaces. The method is applied to Sentinel-6 Unfocused SAR (UFSAR) and Fully Focused SAR (FFSAR) data, with their corresponding tailored waveform model, referred to as the SAR_LIT and FFSAR_LIT retracker, respectively. We found that LIT retrievals from Sentinel-6 high-resolution SAR data at different posting rates are fully consistent with the LIT estimations obtained from thermodynamic lake ice model simulations and from low-resolution mode (LRM) Sentinel-6 and Jason-3 data over two ice seasons during the tandem phase of the two satellites, demonstrating the continuity between LRM and SAR LIT retrievals. By comparing the Sentinel-6 SAR LIT estimates to optical/radar images, we found that the Sentinel-6 LIT measurements are fully consistent with the evolution of the lake surface conditions, accurately capturing the seasonal transitions of ice formation and melt. The uncertainty in the LIT estimates obtained with Sentinel-6 UFSAR data at 20 Hz is in the order of 5 cm, meeting the GCOS requirements for LIT measurements. This uncertainty is significantly smaller, by a factor of 2 to 3 times, than the uncertainty obtained with LRM data. The FFSAR processing at 140 Hz provides even better LIT estimates, with 20% smaller uncertainties. The LIT retracker analysis performed on data at the higher posting rate (140 Hz) shows increased performance in comparison to the 20 Hz data, especially during the melt transition period, due to the increased statistics. The LIT analysis has been performed over two representative lakes, Great Slave Lake and Baker Lake (Canada), demonstrating that the results are robust and hold for lake targets that differ in terms of size, bathymetry, snow/ice properties, and seasonal evolution of LIT. The SAR LIT retrackers presented are promising tools for monitoring the inter-annual variability and trends in LIT from current and future SAR altimetry missions. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

Climate warming causes shorter winters and changes in ice and snow cover in subarctic lakes, highlighting the need to better understand under-ice ecosystem functioning. The plankton community in a subarctic, oligotrophic lake was studied throughout the ice-covered season, focusing on lipid dynamics and life history traits in two actively overwintering copepods, Cyclops scutifer and Eudiaptomus graciloides. Whereas C. scutifer was overwintering in C-IV to C-V stage, E. graciloides reproduced under ice cover. Both species had accumulated lipids prior to ice-on and showed a substantial decrease in total lipid content throughout the ice-covered period: E. graciloides (60%–38% dw) and C. scutifer (73%–33% dw). Polyunsaturated fatty acids of algal origin were highest in E. graciloides and declined strongly in both species. Stearidonic acid (18:4n-3) content in E. graciloides was particularly high and decreased rapidly during the study period by 50%, probably due to reproduction. The copepods differed in feeding behavior, with the omnivore C. scutifer continuing to accumulate lipids until January, whereas the herbivorous E. graciloides accumulated lipids from under-ice primary production during the last months of ice-cover. Our findings emphasize the importance of lipid accumulation and utilization for actively overwintering copepods irrespective of the timing of their reproduction. [ABSTRACT FROM AUTHOR]

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 ice phenology of alpine lakes on the Tibetan Plateau (TP) is a rapid and direct responder to climate changes, and the variations in lake ice exhibit high temporal frequency characteristics. MODIS and passive microwave data are widely used to monitor lake ice changes with high temporal resolution. However, the low spatial resolutions make it difficult to effectively quantify the freeze-melt dynamics of lakes. This work used Sentinel-1 synthetic aperture radar (SAR) data to derive highresolution ice maps (about 6 days), then with the aid of Sentinel-2 optical images to quantify freeze-melt processes in three typical lakes on the TP (e.g. Selin Co, Ayakekumu Lake, and Nam Co). The results showed that three lakes had an average annual ice period of 125–157 days and a complete ice cover period of 72–115 days, from 2018 to 2022. They exhibit different ice phenology patterns. Nam Co is characterized by repeated episodes of freezing, melting, and refreezing, resulting in a prolonged freeze-up period. Meanwhile, the break-up period of Nam Co lasts for a longer duration (about 19 days), and the break-up exhibits a smooth process. Similarly, Ayakekumu Lake showed more significant inter-annual fluctuations in the freeze-up period, with deviations of up to 28 days observed among different years. Compared to the other two lakes, Selin Co experienced a relatively short freeze-up and break-up period. In short, Sentinel-1 SAR data can effectively monitor the weekly and seasonal variations in lake ice on the TP. Particularly, this data facilitates quantification of the freeze-melt dynamics. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Field observations were performed in Lake Nanhai, a shallow Chinese lake, to investigate the impact of variations in snow thickness (0–7.0 cm) on solar radiation transfer. As the snow thickness increases from 0 to 7.0 cm, the albedo increases from 0.27 to 0.96. The bulk extinction coefficients of snow, snow-ice, and congelation ice are 9.47, 9.69, and 2.18 m−1, respectively. The peak of the transmission spectrum shifts from the blue to green light waveband compared to the solar radiation spectrum. The proportion of incident radiation at surface penetrating to the under-ice water ranges from 0.5% to 19.7% associated with snow depth from 7 cm to 0 cm. Fresh snow influences the under-ice light condition crucially, and therefore the seasonal evolution of the lake phytoplankton community is affected. [ABSTRACT FROM AUTHOR]

To investigate changes in ice thickness, ice temperature, and ice phenology characteristics in Lake Ulansu under the influence of climate warming, the complete process of ice formation and melting of Lake Ulansu from 2015 to 2016 was simulated using the HIGHTSI model. We used the ERA5-Land and MERRA-2 reanalysis data sets for atmospheric forcing, which were combined with initial simulation data obtained from multisource satellite remote sensing images. It was found that: ① During the study period, the maximum ice thickness was up to 41. 7 cm, with freeze- up start and break- up end dates of November 21, 2015 and March 25, 2016, respectively, and an ice cover duration of 126 d. ② For Lake Ulansu, which had a shallow water depth and sufficient winter sunshine, air temperature was the main factor affecting ice thickness, followed by solar radiation, and the diurnal cycles of the two significantly affecting the thickness and temperature of the ice layer. However, when the ice was covered with snow, the influence of air temperature and solar radiation on the ice layer was significantly weakened due to the low thermal conductivity and high albedo of snow. ③ Even in the absence of field observation data, the use of meteorological data and initial simulation data derived from remote sensing could still be used to characterize the complete evolution of real snow and ice in the field accurately. This study provided the foundation for the calculation of seasonal ice cover on shallow lake ice in midlatitude arid regions and for investigations of interannual variations in lake ice formation and melting. [ABSTRACT FROM AUTHOR]

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.

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.

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

We found that a winter of abnormally low snowfall and numerous dust storms from eolian processes acting on exposed landscapes (including a major 4-day dust storm while onsite in May 2014) caused a cascade of impacts on the physical, chemical, and ecological functioning of the largest lake by volume in the High Arctic (Lake Hazen; Nunavut, Canada). MODIS imagery revealed that dust deposited in snowpacks on the lake's ice acted as light-absorbing impurities (LAIs), reducing surface reflectance and increasing surface temperatures relative to normal snowpack years, causing early snowmelt and drainage of meltwaters into the lake. LAIs remaining on the ice surface melted into the ice, causing premature candling and one of the earliest ice-offs and longest ice-free seasons on record for Lake Hazen. Meltwater inputs from snowpacks resulted in dilution of dissolved, and increased concentration of particulate bound, chemical species in Lake Hazen's upper water column. Spring inputs of nutrients increased both heterotrophy and algal productivity under the surface ice following snowmelt, with a net consumption of dissolved oxygen. As climate change continues to alter High Arctic temperatures and precipitation patterns, we can expect further changes in dust storm frequency and severity with corresponding impacts for freshwater ecosystems.