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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.

Lakes widely distribute in the Qinghai-Xizang Plateau， and most of them are seasonally frozen lakes.Under the background of global warming， lake ice thickness and phenology are changing significantly， which has a profound impact on regional climate evolution.However， the evolution characteristics of ice thickness and phenology on the climatological scale are not well understood at present.Therefore， in this paper， the lake ice thickness and phenological evolution characteristics of Qinghai Lake during 1979 -2017 were studied by using the field lake ice observation data from Qinghai Lake Xiashe Hydrology Station， MODIS Lake ice coverage dataset， meteorological observation data from Gangcha Meteorological Station and CMFD， combined with a quasi-steady state model of lake ice.The results show that the simulated average ice thickness is 0.31 m， which is close to the measured value of Xiashe Hydrology Station.The error in modelling breaking-up end is only 0.07 days， and the errors of the freezing-up start and the ice duration are 5.60 days and 5.67 days， respectively.The simulated maximum ice thickness decreases from 1979 to 2017 is in good agreement with the observed trend， that is， the ice thickness decreases by 0.003 m per year.In the freezing periods from 1979 to 2017， the freezing-up start of Qinghai Lake is delayed （0.23 d·a-1）， the breaking-up end is advanced （0.32 d·a-1）， and the length of the ice duration is shortened （1.02 d·a-1）， especially in the 1980s （2.2 d·a-1）.During the freezing periods of Qinghai Lake from 1979 to 2017 （from December to April of the following year）， the downward longwave radiation and air temperature （both of which showed an increasing trend） have a significant negative correlation with the average ice thickness and the maximum ice thickness， while the downward shortwave radiation （which showed a decreasing trend） has a significant positive correlation with the maximum ice thickness and the average ice thickness.The detrending sensitivity test shows that： downward longwave radiation， air temperature， downward shortwave radiation and specific humidity are the main driving factors of mean ice thickness and maximum ice thickness variability in Qinghai Lake from 1979 to 2017， contributing 42.08%， 40.93%， -36.99% and 17.45% to mean ice thickness variability， and 44.48%， 44.68%， -34.77% and 19.92% to maximum ice thickness variability， respectively.All the meteorological driving factors contribute 83.40% and 87.01% to the two factors.It can be seen that the maximum ice thickness variability of Qinghai Lake is more susceptible to the influence of meteorological conditions than the average ice thickness variability.The results of this study provide an understanding of the long-term evolution trend of lake ice in the cryosphere， and provide a reference for the study of other lakes in the Tibetan Plateau in the cold season.

Simulating the ice phenology of deep alpine lakes is important and challenging in coupled atmosphere–lake models. In this study, the Weather Research and Forecasting (WRF) model, coupled with two lake models, the freshwater lake (WRF–FLake) model and the default lake (WRF–CLake) model, was applied to Nam Co, a typical deep alpine lake located in the centre of the Tibetan Plateau, to simulate its lake ice phenology. Due to the large errors in simulating lake ice phenology, related key parameters and parameterizations were improved in the coupled model based on observations and physics-based schemes. By improving the momentum, hydraulic, and thermal roughness length parameterizations, both the WRF–FLake model and the WRF–CLake model reasonably simulated the lake freeze-up date. By improving the key parameters associated with shortwave radiation transfer processes when lake ice exists, both models generally simulated the lake break-up date well. Compared with WRF–CLake without improvements, the coupled model with both revised lake models significantly improved the simulation of lake ice phenology. However, there were still considerable errors in simulating the spatial patterns of freeze-up and break-up dates, implying that significant challenges in simulating the lake ice phenology still exist in representing some important model physics, including lake physics such as grid-scale water circulation and atmospheric processes such as snowfall and surface snow dynamics. Therefore, this work can provide valuable new implications for advancing lake ice phenology simulations in coupled models, and the improved model also has practical application prospects in weather and climate forecasts.

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.

Lake ice phenology serves as a sensitive indicator of climate change in the lake-rich Northeast China. In this study, the freeze-up date (FUD), break-up date (BUD), and ice cover duration (ICD) of 31 lakes were extracted from a time series of the land water surface temperature (LWST) derived from the combined MOD11A1 and MYD11A1 products for the hydrological years 2001 to 2021. Our analysis showed a high correlation between the ice phenology measures derived by our study and those provided by hydrological records (R2 of 0.89) and public datasets (R2 > 0.7). There was a notable coherence in lake ice phenology in Northeast China, with a trend in later freeze-up (0.21 days/year) and earlier break-up (0.19 days/year) dates, resulting in shorter ice cover duration (0.50 days/year). The lake ice phenology of freshwater lakes exhibited a faster rate of change compared to saltwater lakes during the period from HY2001 to HY2020. We used redundancy analysis and correlation analysis to study the relationships between the LWST and lake ice phenology with various influencing factors, including lake properties, local climate factors, and atmospheric circulation. Solar radiation, latitude, and air temperature were found to be the primary factors. The FUD was more closely related to lake characteristics, while the BUD was linked to local climate factors. The large-scale oscillations were found to influence the changes in lake ice phenology via the coupled influence of air temperature and precipitation. The Antarctic Oscillation and North Atlantic Oscillation correlate more with LWST in winter, and the Arctic Oscillation correlates more with the ICD.

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.

Lake ice is sensitive to climate change.Under the background of global warming， lake ice is generally declining， and even some lakes in the Northern Hemisphere may not freeze in the future.Lakes are widely distributed in the Qinghai-Xizang Plateau， but there are few studies on lake ice simulation.In addition， the understanding of lake ice characteristics and its freezing coefficients on the Qinghai-Xizang Plateau is still relatively limited.The freezing coefficient A0 of Qinghai Lake was calculated by the degree-day approach for ice growth， using the lake ice thickness measurement data from fly-by-wire radar， meteorological observation data of Qinghai Lake， ERA5-Land reanalysis data， MODIS land surface temperature data and Aqua/MODIS satellite remote sensing images.The results show that A0 has spatial and temporal heterogeneity in Qinghai Lake， and the former is more obvious than the latter.Both the degree-day approach and the energy balance-based analytical model are applied to simulate the lake ice evolution of Qinghai Lake on the Qinghai-Xizang Plateau.The results show that， compared with the degree-day method， the simulations of the start of ice formation， the end of melting and the thickness of lake ice for the 2018 -2019 ice season of Qinghai Lake by the energy balance-based analytic Model are more consistent with the observations.The analytical model is a better method than the degree-day method for simulating the ice of Qinghai Lake.It incorporates the effects of radiation， energy exchange， meteorological elements， lake depth and other factors， thus improving the physical processes and compensating for the fact that the accuracy of the degree-day method of simulation depends on the first date of freezing and cannot simulate the melting process.

Synthetic aperture radar (SAR) data from the Sentinel-1 (S1) mission with its high temporal and spatial resolution allows for an automated detection of lake ice break-up timings from surface backscatter differences across south (S), southwest (SW), and northwest (NW) Greenland (<71° N latitude) during the period 2017 to 2021. Median break-up dates of the 563 studied lakes range between 8 June and 10 July, with the earliest being in 2019 and the latest in 2018. There is a strong correlation between the break-up date and elevation, while a weak relationship with latitude and lake area could be observed. Lake-specific median break-up timings for 2017–2021 increase (i.e., are later) by 3 d per 100 m elevation gain. When assuming an earlier break-up timing of 8 d which corresponds to the observed median variability of ±8 d, the introduced excess energy due to a changing surface albedo from snow-covered ice surface to water translates to melting 0.4 ± 0.1 m thick ice at the melting point or heating up a water depth down to 35 ± 3 m by 1 K across the entire surface area of each respective lake. Upscaling the results to 100 486 lakes across the S, SW, and NW regions, which correspond to 64.5 % of all lakes or 62.1 % of the overall lake area in Greenland, yields an estimate of 1.8 × 106 TJ additional energy input. This translates to melting 5.8 Gt ice at the melting point or warming 432.3 Gt water by 1 K.

Abstract Recruiting the public to participate in the scientific process can be invaluable in furthering our understanding of global environmental change. Several long‐term citizen science projects have been active for over a decade, with most involving the public in the data collection phase of the scientific process. Our team has recently inherited a long‐term citizen science project called the Community Lake Ice Collaboration (CLIC). We have benefited from engaging with a community of experienced participants who have collected reliable data for hundreds of lakes across the USA for over 30 yr. Collecting data at this scale would have been logistically and financially challenging without participants volunteering their time and effort. Here, we aim to provide an overview of the lessons we have learned from CLIC and other citizen science projects and develop nine guidelines for establishing and sustaining a long‐term citizen science project.

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.

Lake ice phenology serves as a sensitive indicator of climate change in the lake-rich Northeast China. In this study, the freeze-up date (FUD), break-up date (BUD), and ice cover duration (ICD) of 31 lakes were extracted from a time series of the land water surface temperature (LWST) derived from the combined MOD11A1 and MYD11A1 products for the hydrological years 2001 to 2021. Our analysis showed a high correlation between the ice phenology measures derived by our study and those provided by hydrological records (R2 of 0.89) and public datasets (R2 > 0.7). There was a notable coherence in lake ice phenology in Northeast China, with a trend in later freeze-up (0.21 days/year) and earlier break-up (0.19 days/year) dates, resulting in shorter ice cover duration (0.50 days/year). The lake ice phenology of freshwater lakes exhibited a faster rate of change compared to saltwater lakes during the period from HY2001 to HY2020. We used redundancy analysis and correlation analysis to study the relationships between the LWST and lake ice phenology with various influencing factors, including lake properties, local climate factors, and atmospheric circulation. Solar radiation, latitude, and air temperature were found to be the primary factors. The FUD was more closely related to lake characteristics, while the BUD was linked to local climate factors. The large-scale oscillations were found to influence the changes in lake ice phenology via the coupled influence of air temperature and precipitation. The Antarctic Oscillation and North Atlantic Oscillation correlate more with LWST in winter, and the Arctic Oscillation correlates more with the ICD. [ABSTRACT FROM AUTHOR]

Lake ice phenology directly reflects local climate changes, serving as a key indicator of climate change. In today's rapidly evolving climate, utilizing advanced remote sensing techniques to quickly extract long-term lake ice phenology features and studying their correlation with other climate factors is crucial. This study focuses on lakes in Xinjiang, China, with a mountainous area greater than 100 km2, including Sayram Lake, Ayahkum Lake, Achihkul Lake, Jingyu Lake, and Ahsaykan Lake. The Bayesian ensemble change detection algorithm was employed to extract lake ice phenology information, and the Mann–Kendall (MK) non-parametric test was used to analyze trends. The visual interpretation method was used to interpret the spatial evolution characteristics of lake ice, and the Pearson correlation coefficient was used to explore the driving factors of lake ice phenology. Results indicate the following: (1) Jingyu Lake exhibited the most significant delay in both freezing and complete freezing days, while Ayahkum Lake showed the most stable pattern. Ahsaykan Lake demonstrated the least delay in both starting and complete melting days. (2) Sayram Lake's ice evolution was unstable, with wind causing variability in the locations where freezing begins and melting spreading from the west shore. Ayahkum Lake, Ahsaykan Lake, and Jingyu Lake exhibited similar seasonal variations, while Achihkul Lake's ice spatial changes spread from the east to the center during freezing and from the center to the shore during melting. (3) The study found that the freeze–thaw process is influenced by a combination of factors including lake area, precipitation, wind speed, and temperature. [ABSTRACT FROM AUTHOR]

Monitoring of lake ice is important to maintain transportation routes, but in recent decades the number of in situ observations have declined. Remote sensing has worked to fill this gap in observations, with active microwave sensors, particularly synthetic-aperture radar (SAR), being a crucial technology. However, the impact of wet conditions on radar and how interactions change under these conditions have been largely ignored. It is important to understand these interactions as warming conditions are likely to lead to an increase in the occurrence of slush layers. This study works to address this gap using the Snow Microwave Radiative Transfer (SMRT) model to conduct forward-modelling experiments of backscatter for Lake Oulujärvi in Finland. Experiments were conducted under dry conditions, under moderate wet conditions, and under saturated conditions. These experiments reflected field observations during the 2020–2021 ice season. Results of the dry-snow experiments support the dominance of surface scattering from the ice–water interface. However, conditions where layers of wet snow are introduced show that the primary scattering interface changes depending on the location of the wet layer. The addition of a saturated layer at the ice surface results in the highest backscatter values due to the larger dielectric contrast created between the overlying dry snow and the slush layer. Improving the representation of these conditions in SMRT can also aid in more accurate retrievals of lake ice properties such as roughness, which is key for inversion modelling of other properties such as ice thickness.

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

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.

Simulating the ice phenology of deep alpine lakes is important and challenging in coupled atmosphere–lake models. In this study, the Weather Research and Forecasting (WRF) model, coupled with two lake models, the freshwater lake (WRF–FLake) model and the default lake (WRF–CLake) model, was applied to Nam Co, a typical deep alpine lake located in the centre of the Tibetan Plateau, to simulate its lake ice phenology. Due to the large errors in simulating lake ice phenology, related key parameters and parameterizations were improved in the coupled model based on observations and physics-based schemes. By improving the momentum, hydraulic, and thermal roughness length parameterizations, both the WRF–FLake model and the WRF–CLake model reasonably simulated the lake freeze-up date. By improving the key parameters associated with shortwave radiation transfer processes when lake ice exists, both models generally simulated the lake break-up date well. Compared with WRF–CLake without improvements, the coupled model with both revised lake models significantly improved the simulation of lake ice phenology. However, there were still considerable errors in simulating the spatial patterns of freeze-up and break-up dates, implying that significant challenges in simulating the lake ice phenology still exist in representing some important model physics, including lake physics such as grid-scale water circulation and atmospheric processes such as snowfall and surface snow dynamics. Therefore, this work can provide valuable new implications for advancing lake ice phenology simulations in coupled models, and the improved model also has practical application prospects in weather and climate forecasts. [ABSTRACT FROM AUTHOR]

The Laurentian Great Lakes significantly influence the climate of the Midwest and Northeast United States, due to their vast thermal inertia, moisture source potential, and unique heat and moisture flux dynamics. This study presents a newly developed coupled lake-ice-atmosphere (CLIAv1) modeling system for the Great Lakes by coupling the National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) regional climate model (RCM) with the three-dimensional (3D) Finite Volume Community Ocean Model (FVCOM) and investigates the impact of coupled dynamics on simulating the Great Lakes' winter climate. By integrating 3D lake hydrodynamics, CLIAv1 addresses the limitations of traditional one-dimensional (1D) lake and demonstrates superior performance in reproducing observed LSTs, ice cover distribution, and the vertical thermal structure of the Great Lakes compared to the NU-WRF model coupled with the default 1D Lake Ice Snow and Sediment Simulator (LISSS). CLIAv1 also enhances simulation of over-lake atmospheric conditions, including air temperature, wind speed, and sensible and latent heat fluxes, underscoring the importance of resolving complex lake dynamics for reliable climate projections. More importantly, this study addresses the crucial question about what are the key processes influencing lake thermal structure and ice cover that are missed by 1D lake models but effectively captured by 3D lake models. Through process-oriented numerical experiments, we identify key 3D hydrodynamic processes – ice transport, heat advection, and shear production in turbulence – that explain the superiority of 3D lake models over 1D lake models, particularly in cold season performance and lake-atmosphere interactions. Properly resolving these processes using 3D hydrodynamic model is crucial for successfully simulating the lake-ice-atmosphere coupled Great Lakes winter system. This research underscores the necessity of incorporating 3D hydrodynamic models in RCMs to improve our predictive understanding of the Great Lakes' response to climate change. The findings advocate for a shift towards high-resolution, physics-based modeling approaches to ensure accurate future climate and limnological projections for large freshwater systems. [ABSTRACT FROM AUTHOR]

Lake ice is an important socio-economic resource that is threatened by climate change. The cover and duration of lake ice are expected to decline as air temperatures warm in the coming decades, disrupting a previously reliable source of income for many activities dependent on lake ice. The economic consequences of climate-induced lake ice loss remain unexplored, creating a significant research gap. The purpose of this study was to quantify the monetary spending associated with lake ice and how climate change may impact that value. Using a series of General Circulation Models (GCMs), greenhouse gas emissions scenarios, and models for lake ice cover, we predicted changes in lake ice by the end of the 21st century for the Northern Hemisphere. We also synthesized examples of spending associated with lake ice activities and discussed the potential implications expected with declining ice cover. We found that lake ice will decrease in area by 44,000–177,000 km2 and shorten in duration by 13–43 days by 2100. Using 31 examples of revenue from lake ice, we found that lake ice generates spending of over USD 2.04 billion to local communities and economies. We also found that countries predicted to experience the greatest ice loss by the end of the century are those that currently have the largest GDP, highest greenhouse gas emissions, and are most dependent on freshwater withdrawal. Our findings confirm predicted losses in lake ice that are expected because of climate change and quantify some of the potential consequences for local communities. Here we highlight lake ice as another casualty of human-caused climate change that will have profound socio-economic implications. [ABSTRACT FROM AUTHOR]

Lakes are rapidly losing ice under global warming, but little is known about ice structure changes. Ice structure is a key regulator of ice stability and thus safety, affecting activities on ice. Here, we analysed spatial and temporal variations in ice structure across 21 Swedish lakes, spanning from 55 to 69 °N, and over five decades. We found regional differences in ice structure, with fastest changes occurring in southern Sweden. The stable clear ice layer was particularly sensitive to warming, showing a rapid decline. The number of days when temperatures exceeded the freezing point during the ice cover period was identified as a strong driver for how ice was structured. Since there is a high risk for increased occurrences of unsafe ice conditions under predicted air temperature changes, we recommend re-establishing ice structure monitoring programmes, informing society on the increased risks of being on ice and including ice structure to safety guidelines., (© 2024. The Author(s).)

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.

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.

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.

Lake ice phenology (LIP) is an essential indicator of climate change and helps with understanding of the regional characteristics of climate change impacts. Ground observation records and remote sensing retrieval products of lake ice phenology are abundant for Europe, North America, and the Tibetan Plateau, but there is a lack of data for inner Eurasia. In this work, enhanced-resolution passive microwave satellite data (PMW) were used to investigate the Northern Hemisphere Lake Ice Phenology (PMW LIP). The Freeze Onset (FO), Complete Ice Cover (CIC), Melt Onset (MO), and Complete Ice Free (CIF) dates were derived for 753 lakes, including 409 lakes for which ice phenology retrievals were available for the period 1978 to 2020 and 344 lakes for which these were available for 2002 to 2020. Verification of the PMW LIP using ground records gave correlation coefficients of 0.93 and 0.84 for CIC and CIF, respectively, and the corresponding values of the RMSE were 11.84 and 10.07 days. The lake ice phenology in this dataset was significantly correlated (P

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]

Lake ice is an important socio-economic resource that is threatened by climate change. The cover and duration of lake ice are expected to decline as air temperatures warm in the coming decades, disrupting a previously reliable source of income for many activities dependent on lake ice. The economic consequences of climate-induced lake ice loss remain unexplored, creating a significant research gap. The purpose of this study was to quantify the monetary spending associated with lake ice and how climate change may impact that value. Using a series of General Circulation Models (GCMs), greenhouse gas emissions scenarios, and models for lake ice cover, we predicted changes in lake ice by the end of the 21st century for the Northern Hemisphere. We also synthesized examples of spending associated with lake ice activities and discussed the potential implications expected with declining ice cover. We found that lake ice will decrease in area by 44,000-177,000 km2 and shorten in duration by 13-43 days by 2100. Using 31 examples of revenue from lake ice, we found that lake ice generates spending of over USD 2.04 billion to local communities and economies. We also found that countries predicted to experience the greatest ice loss by the end of the century are those that currently have the largest GDP, highest greenhouse gas emissions, and are most dependent on freshwater withdrawal. Our findings confirm predicted losses in lake ice that are expected because of climate change and quantify some of the potential consequences for local communities. Here we highlight lake ice as another casualty of human-caused climate change that will have profound socio-economic implications.