Francis Meloche, Francis Gauthier, Alexandre Langlois
Abstract. Snow avalanches represent a natural hazard to infrastructure and backcountry recreationists. Risk assessment of avalanche hazard is difficult due to the sparse nature of available observations informing on snowpack mechanical and geophysical properties and overall stability. The spatial variability of these properties also adds complexity to decision-making and route finding in avalanche terrain for mountain users. Snow cover models can simulate snow mechanical properties with good accuracy at fairly good spatial resolution (around 100 m). However, monitoring small-scale variability at the slope scale (5–50 m) remains critical, since slope stability and the possible size of an avalanche are governed by that scale. To better understand and estimate the spatial variability at the slope scale, this work explores links between snow mechanical properties and microtopographic indicators. Six spatial snow surveys were conducted in two study areas across Canada. Snow mechanical properties, such as snow density, elastic modulus and shear strength, were estimated from high-resolution snow penetrometer (SMP) profiles at multiple locations over several studied slopes, in Rogers Pass, British Columbia, and Mt. Albert, Québec. Point snow stability metrics, such as the skier crack length, critical propagation crack length and a skier stability index, were derived using the snow mechanical properties from SMP measurements. Microtopographic indicators, such as the topographic position index (TPI), vegetation height and proximity, wind-exposed slope index, and potential radiation index, were derived from unoccupied aerial vehicle (UAV) surveys with sub-metre resolution. We computed the variogram and the fractal dimension of the snow mechanical properties and stability metrics and compared them. The comparison showed some similarities in the correlation distances and fractal dimensions between the slab thickness and the slab snow density and also between the weak layer strength and the stability metrics. We then spatially modelled snow mechanical properties, including point snow stability, using spatial generalized additive models (GAMs) with microtopographic indicators as covariates. The use of covariates in GAMs suggested that microtopographic indicators can be used to adequately estimate the variation in the snow mechanical properties but not the stability metrics. We observed a difference in the spatial pattern between the slab and the weak layer that should be considered in snow mechanical modelling.
{"title":"Snow mechanical property variability at the slope scale – implication for snow mechanical modelling","authors":"Francis Meloche, Francis Gauthier, Alexandre Langlois","doi":"10.5194/tc-18-1359-2024","DOIUrl":"https://doi.org/10.5194/tc-18-1359-2024","url":null,"abstract":"Abstract. Snow avalanches represent a natural hazard to infrastructure and backcountry recreationists. Risk assessment of avalanche hazard is difficult due to the sparse nature of available observations informing on snowpack mechanical and geophysical properties and overall stability. The spatial variability of these properties also adds complexity to decision-making and route finding in avalanche terrain for mountain users. Snow cover models can simulate snow mechanical properties with good accuracy at fairly good spatial resolution (around 100 m). However, monitoring small-scale variability at the slope scale (5–50 m) remains critical, since slope stability and the possible size of an avalanche are governed by that scale. To better understand and estimate the spatial variability at the slope scale, this work explores links between snow mechanical properties and microtopographic indicators. Six spatial snow surveys were conducted in two study areas across Canada. Snow mechanical properties, such as snow density, elastic modulus and shear strength, were estimated from high-resolution snow penetrometer (SMP) profiles at multiple locations over several studied slopes, in Rogers Pass, British Columbia, and Mt. Albert, Québec. Point snow stability metrics, such as the skier crack length, critical propagation crack length and a skier stability index, were derived using the snow mechanical properties from SMP measurements. Microtopographic indicators, such as the topographic position index (TPI), vegetation height and proximity, wind-exposed slope index, and potential radiation index, were derived from unoccupied aerial vehicle (UAV) surveys with sub-metre resolution. We computed the variogram and the fractal dimension of the snow mechanical properties and stability metrics and compared them. The comparison showed some similarities in the correlation distances and fractal dimensions between the slab thickness and the slab snow density and also between the weak layer strength and the stability metrics. We then spatially modelled snow mechanical properties, including point snow stability, using spatial generalized additive models (GAMs) with microtopographic indicators as covariates. The use of covariates in GAMs suggested that microtopographic indicators can be used to adequately estimate the variation in the snow mechanical properties but not the stability metrics. We observed a difference in the spatial pattern between the slab and the weak layer that should be considered in snow mechanical modelling.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"125 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140381190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hannah Niehaus, L. Istomina, M. Nicolaus, Ran Tao, Alexey Malinka, E. Zege, G. Spreen
Abstract. The presence of melt ponds on Arctic summer sea ice significantly alters its albedo and thereby the surface energy budget and mass balance. Large-scale observations of melt pond coverage and sea ice albedo are crucial to investigate the role of sea ice for Arctic amplification and its representation in global climate models. We present the new Melt Pond Detection 2 (MPD2) algorithm, which retrieves melt pond, sea ice, and open-ocean fractions as well as surface albedo from Sentinel-3 visible and near-infrared reflectances. In contrast to most other algorithms, our method uses neither fixed values for the spectral albedo of the surface constituents nor an artificial neural network. Instead, it aims for a fully physical representation of the reflective properties of the surface constituents based on their optical characteristics. The state vector X, containing the optical properties of melt ponds and sea ice along with the area fractions of melt ponds and open ocean, is optimized in an iterative procedure to match the measured reflectances and describe the surface state. A major problem in unmixing a compound pixel is that a mixture of half open water and half bright ice cannot be distinguished from a homogeneous pixel of darker ice. In order to overcome this, we suggest constraining the retrieval with a priori information. Initial values and constraint of the surface fractions are derived with an empirical retrieval which uses the same spectral reflectances as implemented in the physical retrieval. The snow grain size and optical thickness change with time, and thus the ice surface albedo changes throughout the season. Therefore, field observations of spectral albedo are used to develop a parameterization of the sea ice optical properties as a function of the temperature history of the sea ice. With these a priori data, the iterative optimization is initialized and constrained, resulting in a retrieval uncertainty of below 8 % for melt pond and 9 % for open-ocean fractions compared to the reference dataset. As reference data for evaluation, a 10 m resolution product of melt pond and open-ocean fraction from Sentinel-2 optical imagery is used.
摘要北极夏季海冰上融池的存在极大地改变了海冰的反照率，从而改变了地表能量预算和质量平衡。融池覆盖率和海冰反照率的大尺度观测对于研究海冰在北极放大效应中的作用及其在全球气候模式中的表现至关重要。我们介绍了新的融池探测 2（MPD2）算法，该算法可从哨兵-3 的可见光和近红外反射率中检索融池、海冰和公海部分以及表面反照率。与其他大多数算法不同的是，我们的方法既不使用地表成分光谱反照率的固定值，也不使用人工神经网络。相反，我们的目标是根据表面成分的光学特性，对其反射特性进行全面的物理表示。状态向量 X 包含融池和海冰的光学特性以及融池和公海的面积比例，通过迭代程序进行优化，以匹配测量到的反射率并描述地表状态。解除混合复合像素的一个主要问题是，无法将一半开放水域和一半明亮冰层的混合物与深色冰层的同质像素区分开来。为了克服这个问题，我们建议使用先验信息对检索进行约束。通过使用与物理检索中相同的光谱反射率进行经验检索，得出了表面分数的初始值和约束条件。雪粒大小和光学厚度随时间变化，因此冰面反照率在整个季节都会发生变化。因此，利用对光谱反照率的实地观测，可将海冰光学特性参数化为海冰温度历史的函数。利用这些先验数据，对迭代优化进行初始化和约束，与参考数据集相比，融池和公海部分的检索不确定性分别低于 8%和 9%。作为评估的参考数据，使用了来自哨兵-2 光学图像的融池和公海部分的 10 米分辨率产品。
{"title":"Melt pond fractions on Arctic summer sea ice retrieved from Sentinel-3 satellite data with a constrained physical forward model","authors":"Hannah Niehaus, L. Istomina, M. Nicolaus, Ran Tao, Alexey Malinka, E. Zege, G. Spreen","doi":"10.5194/tc-18-933-2024","DOIUrl":"https://doi.org/10.5194/tc-18-933-2024","url":null,"abstract":"Abstract. The presence of melt ponds on Arctic summer sea ice significantly alters its albedo and thereby the surface energy budget and mass balance. Large-scale observations of melt pond coverage and sea ice albedo are crucial to investigate the role of sea ice for Arctic amplification and its representation in global climate models. We present the new Melt Pond Detection 2 (MPD2) algorithm, which retrieves melt pond, sea ice, and open-ocean fractions as well as surface albedo from Sentinel-3 visible and near-infrared reflectances. In contrast to most other algorithms, our method uses neither fixed values for the spectral albedo of the surface constituents nor an artificial neural network. Instead, it aims for a fully physical representation of the reflective properties of the surface constituents based on their optical characteristics. The state vector X, containing the optical properties of melt ponds and sea ice along with the area fractions of melt ponds and open ocean, is optimized in an iterative procedure to match the measured reflectances and describe the surface state. A major problem in unmixing a compound pixel is that a mixture of half open water and half bright ice cannot be distinguished from a homogeneous pixel of darker ice. In order to overcome this, we suggest constraining the retrieval with a priori information. Initial values and constraint of the surface fractions are derived with an empirical retrieval which uses the same spectral reflectances as implemented in the physical retrieval. The snow grain size and optical thickness change with time, and thus the ice surface albedo changes throughout the season. Therefore, field observations of spectral albedo are used to develop a parameterization of the sea ice optical properties as a function of the temperature history of the sea ice. With these a priori data, the iterative optimization is initialized and constrained, resulting in a retrieval uncertainty of below 8 % for melt pond and 9 % for open-ocean fractions compared to the reference dataset. As reference data for evaluation, a 10 m resolution product of melt pond and open-ocean fraction from Sentinel-2 optical imagery is used.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"4 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140414507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Frontal ablation has caused 32 %–66 % of Greenland Ice Sheet mass loss since 1972, and despite its importance in driving terminus change, ocean thermal forcing remains crudely incorporated into large-scale ice sheet models. In Greenland, local fjord-scale processes modify the magnitude of thermal forcing at the ice–ocean boundary but are too small scale to be resolved in current global climate models. For example, simulations used in the Ice Sheet Intercomparison Project for CMIP6 (ISMIP6) to predict future ice sheet change rely on the extrapolation of regional ocean water properties into fjords to drive terminus ablation. However, the accuracy of this approach has not previously been tested due to the scarcity of observations in Greenland fjords, as well as the inability of fjord-scale models to realistically incorporate icebergs. By employing the recently developed IceBerg package within the Massachusetts Institute of Technology general circulation model (MITgcm), we here evaluate the ability of ocean thermal forcing parameterizations to predict thermal forcing at tidewater glacier termini. This is accomplished through sensitivity experiments using a set of idealized Greenland fjords, each forced with equivalent ocean boundary conditions but with varying tidal amplitudes, subglacial discharge, iceberg coverage, and bathymetry. Our results indicate that the bathymetric obstruction of external water is the primary control on near-glacier thermal forcing, followed by iceberg submarine melting. Despite identical ocean boundary conditions, we find that the simulated fjord processes can modify grounding line thermal forcing by as much as 3 °C, the magnitude of which is largely controlled by the relative depth of bathymetric sills to the Polar Water–Atlantic Water thermocline. However, using a common adjustment for fjord bathymetry we can still predict grounding line thermal forcing within 0.2 °C in our simulations. Finally, we introduce new parameterizations that additionally account for iceberg-driven cooling that can accurately predict interior fjord thermal forcing profiles both in iceberg-laden simulations and in observations from Kangiata Sullua (Ilulissat Icefjord).
{"title":"Local forcing mechanisms challenge parameterizations of ocean thermal forcing for Greenland tidewater glaciers","authors":"A. Hager, D. Sutherland, D. Slater","doi":"10.5194/tc-18-911-2024","DOIUrl":"https://doi.org/10.5194/tc-18-911-2024","url":null,"abstract":"Abstract. Frontal ablation has caused 32 %–66 % of Greenland Ice Sheet mass loss since 1972, and despite its importance in driving terminus change, ocean thermal forcing remains crudely incorporated into large-scale ice sheet models. In Greenland, local fjord-scale processes modify the magnitude of thermal forcing at the ice–ocean boundary but are too small scale to be resolved in current global climate models. For example, simulations used in the Ice Sheet Intercomparison Project for CMIP6 (ISMIP6) to predict future ice sheet change rely on the extrapolation of regional ocean water properties into fjords to drive terminus ablation. However, the accuracy of this approach has not previously been tested due to the scarcity of observations in Greenland fjords, as well as the inability of fjord-scale models to realistically incorporate icebergs. By employing the recently developed IceBerg package within the Massachusetts Institute of Technology general circulation model (MITgcm), we here evaluate the ability of ocean thermal forcing parameterizations to predict thermal forcing at tidewater glacier termini. This is accomplished through sensitivity experiments using a set of idealized Greenland fjords, each forced with equivalent ocean boundary conditions but with varying tidal amplitudes, subglacial discharge, iceberg coverage, and bathymetry. Our results indicate that the bathymetric obstruction of external water is the primary control on near-glacier thermal forcing, followed by iceberg submarine melting. Despite identical ocean boundary conditions, we find that the simulated fjord processes can modify grounding line thermal forcing by as much as 3 °C, the magnitude of which is largely controlled by the relative depth of bathymetric sills to the Polar Water–Atlantic Water thermocline. However, using a common adjustment for fjord bathymetry we can still predict grounding line thermal forcing within 0.2 °C in our simulations. Finally, we introduce new parameterizations that additionally account for iceberg-driven cooling that can accurately predict interior fjord thermal forcing profiles both in iceberg-laden simulations and in observations from Kangiata Sullua (Ilulissat Icefjord).\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"14 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140421110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. The Sentinel-1A/B synthetic aperture radar (SAR) imagery archive between 14 October 2014 and 29 June 2023 was used in combination with a segmentation algorithm to create a series of binary ice/open-water maps of Hornsund fjord, Svalbard, at 50 m resolution for nine seasons (2014/15 to 2022/23). The near-daily (1.57 d mean temporal resolution) maps were used to calculate sea ice coverage for the entire fjord and its parts, namely the main basin and three major bays: Burgerbukta, Brepollen and Samarinvågen. The average length of the sea ice season was 158 d (range: 105–246 d). Drift ice first arrived from the southwest between October and March, and the fast-ice onset was on average 24 d later. The fast ice typically disappeared in June, around 20 d after the last day with drift ice. The average sea ice coverage over the sea ice season was 41 % (range: 23 %–56 %), but it was lower in the main basin (27 %) compared to in the bays (63 %). Of the bays, Samarinvågen had the highest sea ice coverage (69 %), likely due to its narrow opening and its location in southern Hornsund protecting it from the incoming wind-generated waves. Seasonally, the highest sea ice coverage was observed in April for the entire fjord and the bays and in March for the main basin. The 2014/15, 2019/20 and 2021/22 seasons were characterised by the highest sea ice coverage, and these were also the seasons with the largest number of negative air temperature days in October–December. The 2019/20 season was characterised by the lowest mean daily and monthly air temperatures. We observed a remarkable interannual variability in the sea ice coverage, but on a nine-season scale we did not record any gradual trend of decreasing sea ice coverage. These high-resolution data can be used to, e.g. better understand the spatiotemporal trends in the sea ice distribution in Hornsund, facilitate comparison between Svalbard fjords, and improve modelling of nearshore wind wave transformation and coastal erosion.
{"title":"Extent, duration and timing of the sea ice cover in Hornsund, Svalbard, from 2014–2023","authors":"Z. Swirad, A. M. Johansson, Eirik Malnes","doi":"10.5194/tc-18-895-2024","DOIUrl":"https://doi.org/10.5194/tc-18-895-2024","url":null,"abstract":"Abstract. The Sentinel-1A/B synthetic aperture radar (SAR) imagery archive between 14 October 2014 and 29 June 2023 was used in combination with a segmentation algorithm to create a series of binary ice/open-water maps of Hornsund fjord, Svalbard, at 50 m resolution for nine seasons (2014/15 to 2022/23). The near-daily (1.57 d mean temporal resolution) maps were used to calculate sea ice coverage for the entire fjord and its parts, namely the main basin and three major bays: Burgerbukta, Brepollen and Samarinvågen. The average length of the sea ice season was 158 d (range: 105–246 d). Drift ice first arrived from the southwest between October and March, and the fast-ice onset was on average 24 d later. The fast ice typically disappeared in June, around 20 d after the last day with drift ice. The average sea ice coverage over the sea ice season was 41 % (range: 23 %–56 %), but it was lower in the main basin (27 %) compared to in the bays (63 %). Of the bays, Samarinvågen had the highest sea ice coverage (69 %), likely due to its narrow opening and its location in southern Hornsund protecting it from the incoming wind-generated waves. Seasonally, the highest sea ice coverage was observed in April for the entire fjord and the bays and in March for the main basin. The 2014/15, 2019/20 and 2021/22 seasons were characterised by the highest sea ice coverage, and these were also the seasons with the largest number of negative air temperature days in October–December. The 2019/20 season was characterised by the lowest mean daily and monthly air temperatures. We observed a remarkable interannual variability in the sea ice coverage, but on a nine-season scale we did not record any gradual trend of decreasing sea ice coverage. These high-resolution data can be used to, e.g. better understand the spatiotemporal trends in the sea ice distribution in Hornsund, facilitate comparison between Svalbard fjords, and improve modelling of nearshore wind wave transformation and coastal erosion.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140427767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Justin Murfitt, Claude Duguay, G. Picard, Juha Lemmetyinen
Abstract. 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.
摘要监测湖冰对维护运输路线非常重要，但近几十年来，现场观测的数量却在减少。遥感技术一直在努力填补这一观测空白，其中有源微波传感器，特别是合成孔径雷达（SAR）是一项重要技术。然而，潮湿条件对雷达的影响以及在这些条件下相互作用的变化在很大程度上被忽视了。了解这些相互作用非常重要，因为气候变暖很可能导致泥泞层的增加。本研究利用雪微波辐射传输（SMRT）模型对芬兰奥卢杰尔维湖的后向散射进行了前向模拟实验，以弥补这一不足。实验在干燥、中度潮湿和饱和的条件下进行。这些实验反映了 2020-2021 年冰季期间的实地观测结果。干雪实验结果支持冰水界面表面散射的主导地位。然而，在引入湿雪层的条件下，主要散射界面会根据湿雪层的位置发生变化。在冰表面添加饱和层会导致最高的反向散射值，这是因为上覆干雪和泥泞层之间产生了较大的介电对比。在 SMRT 中改进这些条件的表示也有助于更准确地检索湖冰属性（如粗糙度），这对于冰厚度等其他属性的反演建模至关重要。
{"title":"Forward modelling of synthetic-aperture radar (SAR) backscatter during lake ice melt conditions using the Snow Microwave Radiative Transfer (SMRT) model","authors":"Justin Murfitt, Claude Duguay, G. Picard, Juha Lemmetyinen","doi":"10.5194/tc-18-869-2024","DOIUrl":"https://doi.org/10.5194/tc-18-869-2024","url":null,"abstract":"Abstract. 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.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"50 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140429760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Voordendag, Brigitta Goger, R. Prinz, T. Sauter, T. Mölg, Manuel Saigger, Georg Kaser
Abstract. Wind-driven snow redistribution affects the glacier mass balance by eroding or depositing mass from or to different parts of the glacier’s surface. High-resolution observations are used to test the ability of large-eddy simulations as a tool for distributed mass balance modeling. We present a case study of observed and simulated snow redistribution over Hintereisferner glacier (Ötztal Alps, Austria) between 6 and 9 February 2021. Observations consist of three high-resolution digital elevation models (Δx=1 m) derived from terrestrial laser scans taken shortly before, directly after, and 15 h after snowfall. The scans are complemented by datasets from three on-site weather stations. After the snowfall event, we observed a snowpack decrease of 0.08 m on average over the glacier. The decrease in the snow depth can be attributed to post-snowfall compaction and the wind-driven redistribution of snow. Simulations were performed with the Weather Research and Forecasting (WRF) model at Δx=48 m with a newly implemented snow drift module. The spatial patterns of the simulated snow redistribution agree well with the observed generalized patterns. Snow redistribution contributed −0.026 m to the surface elevation decrease over the glacier surface on 8 February, resulting in a mass loss of −3.9 kg m−2, which is on the same order of magnitude as the observations. With the single case study we cannot yet extrapolate the impact of post-snowfall events on the seasonal glacier mass balance, but the study shows that the snow drift module in WRF is a powerful tool to improve knowledge on wind-driven snow redistribution patterns over glaciers.
{"title":"A novel framework to investigate wind-driven snow redistribution over an Alpine glacier: combination of high-resolution terrestrial laser scans and large-eddy simulations","authors":"A. Voordendag, Brigitta Goger, R. Prinz, T. Sauter, T. Mölg, Manuel Saigger, Georg Kaser","doi":"10.5194/tc-18-849-2024","DOIUrl":"https://doi.org/10.5194/tc-18-849-2024","url":null,"abstract":"Abstract. Wind-driven snow redistribution affects the glacier mass balance by eroding or depositing mass from or to different parts of the glacier’s surface. High-resolution observations are used to test the ability of large-eddy simulations as a tool for distributed mass balance modeling. We present a case study of observed and simulated snow redistribution over Hintereisferner glacier (Ötztal Alps, Austria) between 6 and 9 February 2021. Observations consist of three high-resolution digital elevation models (Δx=1 m) derived from terrestrial laser scans taken shortly before, directly after, and 15 h after snowfall. The scans are complemented by datasets from three on-site weather stations. After the snowfall event, we observed a snowpack decrease of 0.08 m on average over the glacier. The decrease in the snow depth can be attributed to post-snowfall compaction and the wind-driven redistribution of snow. Simulations were performed with the Weather Research and Forecasting (WRF) model at Δx=48 m with a newly implemented snow drift module. The spatial patterns of the simulated snow redistribution agree well with the observed generalized patterns. Snow redistribution contributed −0.026 m to the surface elevation decrease over the glacier surface on 8 February, resulting in a mass loss of −3.9 kg m−2, which is on the same order of magnitude as the observations. With the single case study we cannot yet extrapolate the impact of post-snowfall events on the seasonal glacier mass balance, but the study shows that the snow drift module in WRF is a powerful tool to improve knowledge on wind-driven snow redistribution patterns over glaciers.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"23 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140436153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jun Xie, Xiangfang Zeng, C. Liang, S. Ni, R. Chu, F. Bao, Rongbing Lin, Benxin Chi, Hao Lv
Abstract. Studying seismic sources and wave propagation in ice plates can provide valuable insights into understanding various processes, such as ice structure dynamics, migration, fracture mechanics and mass balance. However, the harsh environment makes it difficult to conduct in situ dense seismic observations. Consequently, our understanding of the dynamic changes within the ice sheet remains insufficient. We conducted a seismic experiment using a distributed acoustic sensing (DAS) array on a frozen lake, exciting water vibrations through underwater airgun shots. By employing an artificial intelligence method, we were able to detect seismic events that include both high-frequency icequakes and low-frequency events. The icequakes clustered along ice fractures and their activity correlated with local temperature variations. The waveforms of low-frequency events exhibit characteristics of flexural-gravity waves, which offers insights into the properties of the ice plate. Our study demonstrates the effectiveness of an DAS array as an in situ dense seismic network for investigating the internal failure process and dynamic deformation of ice plates such as the ice shelf, which may contribute to an enhanced comprehension and prediction of ice shelf disintegration.
摘要研究冰板中的地震源和波传播可为了解冰结构动力学、迁移、断裂力学和质量平衡等各种过程提供宝贵的见解。然而，由于环境恶劣，很难在现场进行密集的地震观测。因此，我们对冰原内部动态变化的了解仍然不足。我们利用分布式声学传感（DAS）阵列在冰冻湖面上进行了地震实验，通过水下气枪射击激发水体振动。通过采用人工智能方法，我们能够探测到包括高频冰震和低频冰震在内的地震事件。冰震沿着冰裂缝聚集，其活动与当地的温度变化相关。低频事件的波形表现出挠曲重力波的特征，这有助于深入了解冰板的特性。我们的研究证明了 DAS 阵列作为现场密集地震网络在研究冰架等冰板内部破坏过程和动态变形方面的有效性，这可能有助于加强对冰架解体的理解和预测。
{"title":"Ice plate deformation and cracking revealed by an in situ-distributed acoustic sensing array","authors":"Jun Xie, Xiangfang Zeng, C. Liang, S. Ni, R. Chu, F. Bao, Rongbing Lin, Benxin Chi, Hao Lv","doi":"10.5194/tc-18-837-2024","DOIUrl":"https://doi.org/10.5194/tc-18-837-2024","url":null,"abstract":"Abstract. Studying seismic sources and wave propagation in ice plates can provide valuable insights into understanding various processes, such as ice structure dynamics, migration, fracture mechanics and mass balance. However, the harsh environment makes it difficult to conduct in situ dense seismic observations. Consequently, our understanding of the dynamic changes within the ice sheet remains insufficient. We conducted a seismic experiment using a distributed acoustic sensing (DAS) array on a frozen lake, exciting water vibrations through underwater airgun shots. By employing an artificial intelligence method, we were able to detect seismic events that include both high-frequency icequakes and low-frequency events. The icequakes clustered along ice fractures and their activity correlated with local temperature variations. The waveforms of low-frequency events exhibit characteristics of flexural-gravity waves, which offers insights into the properties of the ice plate. Our study demonstrates the effectiveness of an DAS array as an in situ dense seismic network for investigating the internal failure process and dynamic deformation of ice plates such as the ice shelf, which may contribute to an enhanced comprehension and prediction of ice shelf disintegration.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"60 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140444523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Siddharth Singh, Michael Durand, Edward Kim, Ana P. Barros
Abstract. A physical–statistical framework to estimate snow water equivalent (SWE) and snow depth from synthetic aperture radar (SAR) measurements is presented and applied to four SnowSAR flight-line data sets collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, snow density and snow depth, and physical model parameters. Prior distributions are derived from multilayer snow hydrology predictions driven by downscaled numerical weather prediction (NWP) forecasts. To reduce the signal-to-noise ratio, SnowSAR measurements at 1 m resolution were upscaled by simple averaging to 30 and 90 m resolution. To reduce the number of physical parameters, the multilayer snowpack is transformed for Bayesian inference into an equivalent one- or two-layer snowpack with the same snow mass and volume backscatter. Successful retrievals meeting NASEM (2018) science requirements are defined by absolute convergence backscatter errors ≤1.2 dB and local SnowSAR incidence angles between 30 and 45∘ for X- and Ku-band VV-pol backscatter measurements and were achieved for 75 % to 87 % of all grassland pixels with SWE up to 0.7 m and snow depth up to 2 m. SWE retrievals compare well with snow pit observations, showing strong skill in deep snow with average absolute SWE residuals of 5 %–7 % (15 %–18 %) for the two-layer (one-layer) retrieval algorithm. Furthermore, the spatial distributions of snow depth retrievals vis-à-vis lidar estimates have Bhattacharya coefficients above 94 % (90 %) for homogeneous grassland pixels at 30 m (90 m resolution), and values up to 76 % in mixed forest and grassland areas, indicating that the retrievals closely capture snowpack spatial variability. Because NWP forecasts are available everywhere, the proposed approach could be applied to SWE and snow depth retrievals from a dedicated global snow mission.
{"title":"Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17","authors":"Siddharth Singh, Michael Durand, Edward Kim, Ana P. Barros","doi":"10.5194/tc-18-747-2024","DOIUrl":"https://doi.org/10.5194/tc-18-747-2024","url":null,"abstract":"Abstract. A physical–statistical framework to estimate snow water equivalent (SWE) and snow depth from synthetic aperture radar (SAR) measurements is presented and applied to four SnowSAR flight-line data sets collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, snow density and snow depth, and physical model parameters. Prior distributions are derived from multilayer snow hydrology predictions driven by downscaled numerical weather prediction (NWP) forecasts. To reduce the signal-to-noise ratio, SnowSAR measurements at 1 m resolution were upscaled by simple averaging to 30 and 90 m resolution. To reduce the number of physical parameters, the multilayer snowpack is transformed for Bayesian inference into an equivalent one- or two-layer snowpack with the same snow mass and volume backscatter. Successful retrievals meeting NASEM (2018) science requirements are defined by absolute convergence backscatter errors ≤1.2 dB and local SnowSAR incidence angles between 30 and 45∘ for X- and Ku-band VV-pol backscatter measurements and were achieved for 75 % to 87 % of all grassland pixels with SWE up to 0.7 m and snow depth up to 2 m. SWE retrievals compare well with snow pit observations, showing strong skill in deep snow with average absolute SWE residuals of 5 %–7 % (15 %–18 %) for the two-layer (one-layer) retrieval algorithm. Furthermore, the spatial distributions of snow depth retrievals vis-à-vis lidar estimates have Bhattacharya coefficients above 94 % (90 %) for homogeneous grassland pixels at 30 m (90 m resolution), and values up to 76 % in mixed forest and grassland areas, indicating that the retrievals closely capture snowpack spatial variability. Because NWP forecasts are available everywhere, the proposed approach could be applied to SWE and snow depth retrievals from a dedicated global snow mission.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"702 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140446471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Wilson, M. Peternell, F. Salvemini, V. Luzin, F. Enzmann, O. Moravcova, N. Hunter
Abstract. In frozen cylinders composed of deuterium ice (Tm+3.8 ∘C) and 10 % water ice (Tm 0 ∘C), it is possible to track melt pathways produced by increasing the temperature during deformation. Raising the temperature to +2 ∘C produces water (H2O) which combines with the D2O ice to form mixtures of HDO. As a consequence of deformation, HDO and H2O meltwater are expelled along conjugate shear bands and as compactional melt segregations. Melt segregations are also associated with high-porosity networks related to the location of transient reaction fronts where the passage of melt-enriched fluids is controlled by the localized ductile yielding and lowering of the effective viscosity. Accompanying the softening, the meltwater also changes and weakens the crystallographic fabric development of the ice. Our observations suggest meltwater-enriched compaction and shear band initiation provide instabilities and the driving force for an enhancement of permeability in terrestrial ice sheets and glaciers.
{"title":"Partial melting in polycrystalline ice: pathways identified in 3D neutron tomographic images","authors":"C. Wilson, M. Peternell, F. Salvemini, V. Luzin, F. Enzmann, O. Moravcova, N. Hunter","doi":"10.5194/tc-18-819-2024","DOIUrl":"https://doi.org/10.5194/tc-18-819-2024","url":null,"abstract":"Abstract. In frozen cylinders composed of deuterium ice (Tm+3.8 ∘C) and 10 % water ice (Tm 0 ∘C), it is possible to track melt pathways produced by increasing the temperature during deformation. Raising the temperature to +2 ∘C produces water (H2O) which combines with the D2O ice to form mixtures of HDO. As a consequence of deformation, HDO and H2O meltwater are expelled along conjugate shear bands and as compactional melt segregations. Melt segregations are also associated with high-porosity networks related to the location of transient reaction fronts where the passage of melt-enriched fluids is controlled by the localized ductile yielding and lowering of the effective viscosity. Accompanying the softening, the meltwater also changes and weakens the crystallographic fabric development of the ice. Our observations suggest meltwater-enriched compaction and shear band initiation provide instabilities and the driving force for an enhancement of permeability in terrestrial ice sheets and glaciers.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"406 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140448047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Willen, M. Horwath, E. Buchta, M. Scheinert, Veit Helm, B. Uebbing, Jürgen Kusche
Abstract. A detailed understanding of how the Antarctic ice sheet (AIS) responds to a warming climate is needed because it will most likely increase the rate of global mean sea level rise. Time-variable satellite gravimetry, realized by the Gravity Recovery and Climate Experiment (GRACE) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) missions, is directly sensitive to AIS mass changes. However, gravimetric mass balances are subject to two major limitations. First, the usual correction of the glacial isostatic adjustment (GIA) effect by modelling results is a dominant source of uncertainty. Second, satellite gravimetry allows for a resolution of a few hundred kilometres only, which is insufficient to thoroughly explore causes of AIS imbalance. We have overcome both limitations by the first global inversion of data from GRACE and GRACE-FO, satellite altimetry (CryoSat-2), regional climate modelling (RACMO2), and firn densification modelling (IMAU-FDM). The inversion spatially resolves GIA in Antarctica independently from GIA modelling jointly with changes of ice mass and firn air content at 50 km resolution. We find an AIS mass balance of −144 ± 27 Gt a−1 from January 2011 to December 2020. This estimate is the same, within uncertainties, as the statistical analysis of 23 different mass balances evaluated in the Ice sheet Mass Balance Inter-comparison Exercise (IMBIE; Otosaka et al., 2023b). The co-estimated GIA corresponds to an integrated mass effect of 86 ± 21 Gt a−1 over Antarctica, and it fits better with global navigation satellite system (GNSS) results than other GIA predictions. From propagating covariances to integrals, we find a correlation coefficient of −0.97 between the AIS mass balance and the GIA estimate. Sensitivity tests with alternative input data sets lead to results within assessed uncertainties.
{"title":"Globally consistent estimates of high-resolution Antarctic ice mass balance and spatially resolved glacial isostatic adjustment","authors":"M. Willen, M. Horwath, E. Buchta, M. Scheinert, Veit Helm, B. Uebbing, Jürgen Kusche","doi":"10.5194/tc-18-775-2024","DOIUrl":"https://doi.org/10.5194/tc-18-775-2024","url":null,"abstract":"Abstract. A detailed understanding of how the Antarctic ice sheet (AIS) responds to a warming climate is needed because it will most likely increase the rate of global mean sea level rise. Time-variable satellite gravimetry, realized by the Gravity Recovery and Climate Experiment (GRACE) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) missions, is directly sensitive to AIS mass changes. However, gravimetric mass balances are subject to two major limitations. First, the usual correction of the glacial isostatic adjustment (GIA) effect by modelling results is a dominant source of uncertainty. Second, satellite gravimetry allows for a resolution of a few hundred kilometres only, which is insufficient to thoroughly explore causes of AIS imbalance. We have overcome both limitations by the first global inversion of data from GRACE and GRACE-FO, satellite altimetry (CryoSat-2), regional climate modelling (RACMO2), and firn densification modelling (IMAU-FDM). The inversion spatially resolves GIA in Antarctica independently from GIA modelling jointly with changes of ice mass and firn air content at 50 km resolution. We find an AIS mass balance of −144 ± 27 Gt a−1 from January 2011 to December 2020. This estimate is the same, within uncertainties, as the statistical analysis of 23 different mass balances evaluated in the Ice sheet Mass Balance Inter-comparison Exercise (IMBIE; Otosaka et al., 2023b). The co-estimated GIA corresponds to an integrated mass effect of 86 ± 21 Gt a−1 over Antarctica, and it fits better with global navigation satellite system (GNSS) results than other GIA predictions. From propagating covariances to integrals, we find a correlation coefficient of −0.97 between the AIS mass balance and the GIA estimate. Sensitivity tests with alternative input data sets lead to results within assessed uncertainties.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"211 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140448726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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