Ryo Inoue, T. Aoki, Shuji Fujita, S. Tsutaki, Hideaki Motoyama, F. Nakazawa, K. Kawamura
Abstract. To better understand the surface properties of the Antarctic ice sheet, we measured the specific surface area (SSA) of surface snow during two round-trip traverses between a coastal base near Syowa Station, located 15 km inland from the nearest coast, and Dome Fuji, located 1066 km inland, in East Antarctica from November 2021 to January 2022. Using a handheld integrating sphere snow grain sizer (HISSGraS), which directly measures the snow surface without sampling, we collected 215 sets of SSA data, with each set comprising measurements from 10 surfaces along a 20 m transect. The measured SSA shows no elevation or temperature dependence between 15 and 500 km from the coast (elevation: 615–3000 m), with a mean and standard deviation of 25 ± 9 m2 kg−1. Beyond this range, SSA increases toward the interior, reaching 45 ± 11 m2 kg−1 between 800 and 1066 km from the coast (3600–3800 m). SSA shows significant variability depending on surface morphologies and meteorological events. For example, (i) glazed surfaces formed by an accumulation hiatus in katabatic wind areas show low SSA (19 ± 4 m2 kg−1), decreasing the mean SSA and increasing SSA variability. (ii) Freshly deposited snow shows high SSA (60–110 m2 kg−1), but the snow deposition is inhibited by snow drifting at wind speeds above 5 m s−1. Our analyses clarified that temperature-dependent snow metamorphism, snowfall frequency, and wind-driven inhibition of snow deposition play crucial roles in the spatial variation of surface snow SSA in the Antarctic inland. The extensive dataset will enable the validation of satellite-derived and model-simulated SSA variations across Antarctica.
{"title":"Spatial variation in the specific surface area of surface snow measured along the traverse route from the coast to Dome Fuji, Antarctica, during austral summer","authors":"Ryo Inoue, T. Aoki, Shuji Fujita, S. Tsutaki, Hideaki Motoyama, F. Nakazawa, K. Kawamura","doi":"10.5194/tc-18-3513-2024","DOIUrl":"https://doi.org/10.5194/tc-18-3513-2024","url":null,"abstract":"Abstract. To better understand the surface properties of the Antarctic ice sheet, we measured the specific surface area (SSA) of surface snow during two round-trip traverses between a coastal base near Syowa Station, located 15 km inland from the nearest coast, and Dome Fuji, located 1066 km inland, in East Antarctica from November 2021 to January 2022. Using a handheld integrating sphere snow grain sizer (HISSGraS), which directly measures the snow surface without sampling, we collected 215 sets of SSA data, with each set comprising measurements from 10 surfaces along a 20 m transect. The measured SSA shows no elevation or temperature dependence between 15 and 500 km from the coast (elevation: 615–3000 m), with a mean and standard deviation of 25 ± 9 m2 kg−1. Beyond this range, SSA increases toward the interior, reaching 45 ± 11 m2 kg−1 between 800 and 1066 km from the coast (3600–3800 m). SSA shows significant variability depending on surface morphologies and meteorological events. For example, (i) glazed surfaces formed by an accumulation hiatus in katabatic wind areas show low SSA (19 ± 4 m2 kg−1), decreasing the mean SSA and increasing SSA variability. (ii) Freshly deposited snow shows high SSA (60–110 m2 kg−1), but the snow deposition is inhibited by snow drifting at wind speeds above 5 m s−1. Our analyses clarified that temperature-dependent snow metamorphism, snowfall frequency, and wind-driven inhibition of snow deposition play crucial roles in the spatial variation of surface snow SSA in the Antarctic inland. The extensive dataset will enable the validation of satellite-derived and model-simulated SSA variations across Antarctica.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"19 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925302","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}
L. Quéno, R. Mott, Paul Morin, Bertrand Cluzet, G. Mazzotti, Tobias Jonas
Abstract. Snow hydrological regimes in mountainous catchments are strongly influenced by snowpack heterogeneity resulting from wind- and gravity-induced redistribution processes, requiring them to be modelled at hectometre and finer resolutions. This study presents a novel modelling approach to address this issue, aiming at an intermediate-complexity solution to best represent these processes while maintaining operationally viable computational times. To this end, the physics-based snowpack model FSM2oshd was complemented by integrating the modules SnowTran-3D and SnowSlide to represent wind- and gravity-driven redistribution, respectively. This new modelling framework was further enhanced by implementing a density-dependent layering to account for erodible snow without the need to resolve microstructural properties. Seasonal simulations were performed over a 1180 km2 mountain range in the Swiss Alps at 25, 50 and 100 m resolution, using appropriate downscaling and snow data assimilation techniques to provide accurate meteorological forcing. In particular, wind fields were dynamically downscaled using WindNinja to better reflect topographically induced flow patterns. The model results were assessed using snow depths from airborne lidar measurements. We found a remarkable improvement in the representation of snow accumulation and erosion areas, with major contributions from saltation and suspension as well as avalanches and with modest contributions from snowdrift sublimation. The aggregated snow depth distribution curve, key to snowmelt dynamics, significantly and consistently matched the measured distribution better than reference simulations from the peak of winter to the end of the melt season, with improvements at all spatial resolutions. This outcome is promising for a better representation of snow hydrological processes within an operational framework.
{"title":"Snow redistribution in an intermediate-complexity snow hydrology modelling framework","authors":"L. Quéno, R. Mott, Paul Morin, Bertrand Cluzet, G. Mazzotti, Tobias Jonas","doi":"10.5194/tc-18-3533-2024","DOIUrl":"https://doi.org/10.5194/tc-18-3533-2024","url":null,"abstract":"Abstract. Snow hydrological regimes in mountainous catchments are strongly influenced by snowpack heterogeneity resulting from wind- and gravity-induced redistribution processes, requiring them to be modelled at hectometre and finer resolutions. This study presents a novel modelling approach to address this issue, aiming at an intermediate-complexity solution to best represent these processes while maintaining operationally viable computational times. To this end, the physics-based snowpack model FSM2oshd was complemented by integrating the modules SnowTran-3D and SnowSlide to represent wind- and gravity-driven redistribution, respectively. This new modelling framework was further enhanced by implementing a density-dependent layering to account for erodible snow without the need to resolve microstructural properties. Seasonal simulations were performed over a 1180 km2 mountain range in the Swiss Alps at 25, 50 and 100 m resolution, using appropriate downscaling and snow data assimilation techniques to provide accurate meteorological forcing. In particular, wind fields were dynamically downscaled using WindNinja to better reflect topographically induced flow patterns. The model results were assessed using snow depths from airborne lidar measurements. We found a remarkable improvement in the representation of snow accumulation and erosion areas, with major contributions from saltation and suspension as well as avalanches and with modest contributions from snowdrift sublimation. The aggregated snow depth distribution curve, key to snowmelt dynamics, significantly and consistently matched the measured distribution better than reference simulations from the peak of winter to the end of the melt season, with improvements at all spatial resolutions. This outcome is promising for a better representation of snow hydrological processes within an operational framework.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"71 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922501","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. Automated snow station networks provide critical hydrologic data. Whether point observations represent snowpack at larger areas is an enduring question. Leveraging the recent proliferation of airborne lidar snow depth data, we revisit the question of snow station representativeness at multiple scales surrounding 111 stations in Colorado and California (USA) from 2021–2023 (n=476 total samples). In about 50 % of cases, station depths were at least 10 cm higher than areal-mean snow depth (from lidar) at 0.5 to 4 km scales. The nearest 50 m lidar pixels had lower bias and were more often representative of the areal-mean snow depth than coincident stations. The closest 3 m lidar pixel often agreed with station snow depth to within 10 cm, suggesting differences between station snow depth and the nearest 50 m lidar pixel result from highly localized conditions and not the measurement method. Representativeness decreased as scale increased up to ∼6 km, mainly explained by the elevation of a site relative to the larger area. Relative values of vegetation and southness did not have significant impacts on site representativeness. The sign of bias at individual snow stations is temporally consistent, suggesting the relationship between station depth and that of the surrounding area may be predictable. Improving understanding of snow station representativeness could allow for more accurate validation of modeled and remotely sensed data.
{"title":"Reanalyzing the spatial representativeness of snow depth at automated monitoring stations using airborne lidar data","authors":"Jordan N. Herbert, M. Raleigh, Eric E. Small","doi":"10.5194/tc-18-3495-2024","DOIUrl":"https://doi.org/10.5194/tc-18-3495-2024","url":null,"abstract":"Abstract. Automated snow station networks provide critical hydrologic data. Whether point observations represent snowpack at larger areas is an enduring question. Leveraging the recent proliferation of airborne lidar snow depth data, we revisit the question of snow station representativeness at multiple scales surrounding 111 stations in Colorado and California (USA) from 2021–2023 (n=476 total samples). In about 50 % of cases, station depths were at least 10 cm higher than areal-mean snow depth (from lidar) at 0.5 to 4 km scales. The nearest 50 m lidar pixels had lower bias and were more often representative of the areal-mean snow depth than coincident stations. The closest 3 m lidar pixel often agreed with station snow depth to within 10 cm, suggesting differences between station snow depth and the nearest 50 m lidar pixel result from highly localized conditions and not the measurement method. Representativeness decreased as scale increased up to ∼6 km, mainly explained by the elevation of a site relative to the larger area. Relative values of vegetation and southness did not have significant impacts on site representativeness. The sign of bias at individual snow stations is temporally consistent, suggesting the relationship between station depth and that of the surrounding area may be predictable. Improving understanding of snow station representativeness could allow for more accurate validation of modeled and remotely sensed data.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"27 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927351","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}
B. Hmiel, V. Petrenko, C. Buizert, Andrew M. Smith, Michael Dyonisius, P. Place, Bin Yang, Quan Hua, R. Beaudette, J. Severinghaus, C. Harth, Ray F. Weiss, Lindsey Davidge, Melisa Diaz, Matthew Pacicco, J. Menking, M. Kalk, X. Faïn, A. Adolph, I. Vimont, Lee T. Murray
Abstract. Measurements of carbon-14-containing carbon monoxide (14CO) in glacial ice are useful for studies of the past oxidative capacity of the atmosphere as well as for reconstructing the past cosmic ray flux. The 14CO abundance in glacial ice represents the combination of trapped atmospheric 14CO and in situ cosmogenic 14CO. The systematics of in situ cosmogenic 14CO production and retention in ice are not fully quantified, posing an obstacle to interpretation of ice core 14CO measurements. Here we provide the first comprehensive characterization of 14CO at an ice accumulation site (Summit, Greenland), including measurements in the ice grains of the firn matrix, firn air and bubbly ice below the firn zone. The results are interpreted with the aid of a firn gas transport model into which we implemented in situ cosmogenic 14C. We find that almost all (≈ 99.5 %) of in situ 14CO that is produced in the ice grains in firn is very rapidly (in <1 year) lost to the open porosity and from there mostly vented to the atmosphere. The timescale of this rapid loss is consistent with what is expected from gas diffusion through ice. The small fraction of in situ 14CO that initially stays in the ice grains continues to slowly leak out to the open porosity at a rate of ≈ 0.6 % yr−1. Below the firn zone we observe an increase in 14CO content with depth that is due to in situ 14CO production by deep-penetrating muons, confirming recent estimates of 14CO production rates in ice via the muon mechanisms and allowing for narrowing constraints on these production rates.
{"title":"Characterization of in situ cosmogenic 14CO production, retention and loss in firn and shallow ice at Summit, Greenland","authors":"B. Hmiel, V. Petrenko, C. Buizert, Andrew M. Smith, Michael Dyonisius, P. Place, Bin Yang, Quan Hua, R. Beaudette, J. Severinghaus, C. Harth, Ray F. Weiss, Lindsey Davidge, Melisa Diaz, Matthew Pacicco, J. Menking, M. Kalk, X. Faïn, A. Adolph, I. Vimont, Lee T. Murray","doi":"10.5194/tc-18-3363-2024","DOIUrl":"https://doi.org/10.5194/tc-18-3363-2024","url":null,"abstract":"Abstract. Measurements of carbon-14-containing carbon monoxide (14CO) in glacial ice are useful for studies of the past oxidative capacity of the atmosphere as well as for reconstructing the past cosmic ray flux. The 14CO abundance in glacial ice represents the combination of trapped atmospheric 14CO and in situ cosmogenic 14CO. The systematics of in situ cosmogenic 14CO production and retention in ice are not fully quantified, posing an obstacle to interpretation of ice core 14CO measurements. Here we provide the first comprehensive characterization of 14CO at an ice accumulation site (Summit, Greenland), including measurements in the ice grains of the firn matrix, firn air and bubbly ice below the firn zone. The results are interpreted with the aid of a firn gas transport model into which we implemented in situ cosmogenic 14C. We find that almost all (≈ 99.5 %) of in situ 14CO that is produced in the ice grains in firn is very rapidly (in <1 year) lost to the open porosity and from there mostly vented to the atmosphere. The timescale of this rapid loss is consistent with what is expected from gas diffusion through ice. The small fraction of in situ 14CO that initially stays in the ice grains continues to slowly leak out to the open porosity at a rate of ≈ 0.6 % yr−1. Below the firn zone we observe an increase in 14CO content with depth that is due to in situ 14CO production by deep-penetrating muons, confirming recent estimates of 14CO production rates in ice via the muon mechanisms and allowing for narrowing constraints on these production rates.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"31 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802915","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}
T. Maierhofer, A. Flores Orozco, Nathalie Roser, J. K. Limbrock, C. Hilbich, Clemens Moser, A. Kemna, Elisabetta Drigo, Umberto Morra di Cella, C. Hauck
Abstract. We investigate the application of spectral induced polarization (SIP) monitoring to understand seasonal and annual variations in the freeze–thaw processes in permafrost by examining the frequency dependence of subsurface electrical properties. We installed a permanent SIP monitoring profile at a high-mountain permafrost site in the Italian Alps in 2019 and collected SIP data in the frequency range between 0.1–75 Hz over 3 years. The SIP imaging results were interpreted in conjunction with complementary seismic and borehole data sets. In particular, we investigated the phase frequency effect (ϕFE), i.e., the change in the resistivity phase with frequency. We observe that this parameter (ϕFE) is strongly sensitive to temperature changes and might be used as a proxy to delineate spatial and temporal changes in the ice content in the subsurface, providing information not accessible through electrical resistivity tomography (ERT) or single-frequency IP measurements. Temporal changes in ϕFE are validated through laboratory SIP measurements on samples from the site in controlled freeze–thaw experiments. We demonstrate that SIP is capable of resolving temporal changes in the thermal state and the ice / water ratio associated with seasonal freeze–thaw processes. We investigate the consistency between the ϕFE observed in field data and groundwater and ice content estimates derived from petrophysical modeling of ERT and seismic data.
{"title":"Spectral induced polarization imaging to monitor seasonal and annual dynamics of frozen ground at a mountain permafrost site in the Italian Alps","authors":"T. Maierhofer, A. Flores Orozco, Nathalie Roser, J. K. Limbrock, C. Hilbich, Clemens Moser, A. Kemna, Elisabetta Drigo, Umberto Morra di Cella, C. Hauck","doi":"10.5194/tc-18-3383-2024","DOIUrl":"https://doi.org/10.5194/tc-18-3383-2024","url":null,"abstract":"Abstract. We investigate the application of spectral induced polarization (SIP) monitoring to understand seasonal and annual variations in the freeze–thaw processes in permafrost by examining the frequency dependence of subsurface electrical properties. We installed a permanent SIP monitoring profile at a high-mountain permafrost site in the Italian Alps in 2019 and collected SIP data in the frequency range between 0.1–75 Hz over 3 years. The SIP imaging results were interpreted in conjunction with complementary seismic and borehole data sets. In particular, we investigated the phase frequency effect (ϕFE), i.e., the change in the resistivity phase with frequency. We observe that this parameter (ϕFE) is strongly sensitive to temperature changes and might be used as a proxy to delineate spatial and temporal changes in the ice content in the subsurface, providing information not accessible through electrical resistivity tomography (ERT) or single-frequency IP measurements. Temporal changes in ϕFE are validated through laboratory SIP measurements on samples from the site in controlled freeze–thaw experiments. We demonstrate that SIP is capable of resolving temporal changes in the thermal state and the ice / water ratio associated with seasonal freeze–thaw processes. We investigate the consistency between the ϕFE observed in field data and groundwater and ice content estimates derived from petrophysical modeling of ERT and seismic data.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802540","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. Thompson-Munson, Jennifer E. Kay, Bradley R. Markle
Abstract. The porous layer of snow and firn on the Greenland Ice Sheet stores meltwater and limits the rate at which the ice sheet contributes to sea level rise. This buffer is threatened in a warming climate. To better understand the nature and timescales of firn's response to air temperature change on the Greenland Ice Sheet, we use a physics-based model to assess the effects of atmospheric warming and cooling on Greenland's firn air content in idealized climate experiments. We identify an asymmetric response of Greenland's firn to air temperature: firn loses more air content due to warming compared to the amount gained from commensurate cooling. 100 years after a 1 °C temperature perturbation, warming decreases the spatially integrated air content by 9.7 %, and cooling increases it by 8.3 %. In dry firn, this asymmetry is driven by the highly nonlinear relationship between temperature and firn compaction, as well as the dependence of thermal conductivity on the composition of the firn. The influence of liquid water accentuates this asymmetry. In wet firn areas, melt increases nonlinearly with atmospheric warming, thus enhancing firn refreezing and further warming the snowpack through increased latent heat release. Our results highlight the vulnerability of Greenland firn to temperature change and demonstrate that firn air content is more efficiently depleted than generated. This asymmetry in the temperature–firn relationship may contribute to the overall temporally asymmetric mass change of the Greenland Ice Sheet in a changing climate across many timescales.
{"title":"Greenland's firn responds more to warming than to cooling","authors":"M. Thompson-Munson, Jennifer E. Kay, Bradley R. Markle","doi":"10.5194/tc-18-3333-2024","DOIUrl":"https://doi.org/10.5194/tc-18-3333-2024","url":null,"abstract":"Abstract. The porous layer of snow and firn on the Greenland Ice Sheet stores meltwater and limits the rate at which the ice sheet contributes to sea level rise. This buffer is threatened in a warming climate. To better understand the nature and timescales of firn's response to air temperature change on the Greenland Ice Sheet, we use a physics-based model to assess the effects of atmospheric warming and cooling on Greenland's firn air content in idealized climate experiments. We identify an asymmetric response of Greenland's firn to air temperature: firn loses more air content due to warming compared to the amount gained from commensurate cooling. 100 years after a 1 °C temperature perturbation, warming decreases the spatially integrated air content by 9.7 %, and cooling increases it by 8.3 %. In dry firn, this asymmetry is driven by the highly nonlinear relationship between temperature and firn compaction, as well as the dependence of thermal conductivity on the composition of the firn. The influence of liquid water accentuates this asymmetry. In wet firn areas, melt increases nonlinearly with atmospheric warming, thus enhancing firn refreezing and further warming the snowpack through increased latent heat release. Our results highlight the vulnerability of Greenland firn to temperature change and demonstrate that firn air content is more efficiently depleted than generated. This asymmetry in the temperature–firn relationship may contribute to the overall temporally asymmetric mass change of the Greenland Ice Sheet in a changing climate across many timescales.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"90 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807945","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}
Erik Loebel, M. Scheinert, M. Horwath, Angelika Humbert, Julia Sohn, Konrad Heidler, Charlotte Liebezeit, X. Zhu
Abstract. The mass balance of the Greenland Ice Sheet is strongly influenced by the dynamics of its outlet glaciers. Therefore, it is of paramount importance to accurately and continuously monitor these glaciers, especially the variation in their frontal positions. A temporally comprehensive parameterization of glacier calving is essential for understanding dynamic changes and constraining ice sheet modeling. However, many current calving front records are limited in terms of temporal resolution as they rely on manual delineation, which is laborious and not appropriate considering the increasing amount of satellite imagery available. In this contribution, we address this problem by applying an automated method to extract calving fronts from optical satellite imagery. The core of this workflow builds on recent advances in the field of deep learning while taking full advantage of multispectral input information. The performance of the method is evaluated using three independent test datasets. For the three datasets, we calculate mean delineation errors of 61.2, 73.7, and 73.5 m, respectively. Eventually, we apply the technique to Landsat-8 imagery. We generate 9243 calving front positions across 23 outlet glaciers in Greenland for the period 2013–2021. Resulting time series not only resolve long-term and seasonal signals but also resolve subseasonal patterns. We discuss the implications for glaciological studies and present a first application for analyzing the effect of bedrock topography on calving front variations. Our method and derived results represent an important step towards the development of intelligent processing strategies for glacier monitoring, opening up new possibilities for studying and modeling the dynamics of Greenland's outlet glaciers.
{"title":"Calving front monitoring at a subseasonal resolution: a deep learning application for Greenland glaciers","authors":"Erik Loebel, M. Scheinert, M. Horwath, Angelika Humbert, Julia Sohn, Konrad Heidler, Charlotte Liebezeit, X. Zhu","doi":"10.5194/tc-18-3315-2024","DOIUrl":"https://doi.org/10.5194/tc-18-3315-2024","url":null,"abstract":"Abstract. The mass balance of the Greenland Ice Sheet is strongly influenced by the dynamics of its outlet glaciers. Therefore, it is of paramount importance to accurately and continuously monitor these glaciers, especially the variation in their frontal positions. A temporally comprehensive parameterization of glacier calving is essential for understanding dynamic changes and constraining ice sheet modeling. However, many current calving front records are limited in terms of temporal resolution as they rely on manual delineation, which is laborious and not appropriate considering the increasing amount of satellite imagery available. In this contribution, we address this problem by applying an automated method to extract calving fronts from optical satellite imagery. The core of this workflow builds on recent advances in the field of deep learning while taking full advantage of multispectral input information. The performance of the method is evaluated using three independent test datasets. For the three datasets, we calculate mean delineation errors of 61.2, 73.7, and 73.5 m, respectively. Eventually, we apply the technique to Landsat-8 imagery. We generate 9243 calving front positions across 23 outlet glaciers in Greenland for the period 2013–2021. Resulting time series not only resolve long-term and seasonal signals but also resolve subseasonal patterns. We discuss the implications for glaciological studies and present a first application for analyzing the effect of bedrock topography on calving front variations. Our method and derived results represent an important step towards the development of intelligent processing strategies for glacier monitoring, opening up new possibilities for studying and modeling the dynamics of Greenland's outlet glaciers.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"2 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807279","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}
Xinyuan Lv, Zhihao Cui, Ting Wang, Yumin Wen, An Liu, Rusheng Wang
Abstract. Scientific drilling in polar regions plays a crucial role in obtaining ice cores and using them to understand climate change and to study the dynamics of polar ice sheets and their impact on global environmental changes (sea level, ocean current cycle, atmospheric circulation, etc.). Mechanical rotary cutting is a widely used drilling method that drives the cutter to rotate to cut and drill through ice layers. It is necessary to conduct in-depth research on the brittle fracture behavior of ice and mechanical model and to analyze the factors and specific mechanisms (cutter's angle, rotation speed of the drill bit, and cutting depth) affecting cutting force for the rational design of ice core drill systems, improving the efficiency of ice core drilling and ensuring the drilling process runs smoothly. Therefore, in this paper, the process of ice cutting was observed, the fracture mechanics characteristics of the ice cutting process were analyzed, the formation process of ice chips was divided into three stages, and a mathematical model for the cutting force was established based on the observation results. The paper describes the damage conditions of ice failure and points out the factors and specific laws influencing cutting force. Furthermore, the cutting force generated under various experimental conditions was tested. Based on specified real-time variation curves of cutting force, the characteristics of cutting force were analyzed during the cutting and drilling process. Based on comparison to results of the average cutting force, the influence mechanism of various parameters acting on the cutting force was obtained. This proves the correctness of the mathematical model of the cutting force and provides a theoretical reference for the calculation of the cutting force during ice cutting and drilling in polar regions.
{"title":"Research into mechanical modeling based on characteristics of the fracture mechanics of ice cutting for scientific drilling in polar regions","authors":"Xinyuan Lv, Zhihao Cui, Ting Wang, Yumin Wen, An Liu, Rusheng Wang","doi":"10.5194/tc-18-3351-2024","DOIUrl":"https://doi.org/10.5194/tc-18-3351-2024","url":null,"abstract":"Abstract. Scientific drilling in polar regions plays a crucial role in obtaining ice cores and using them to understand climate change and to study the dynamics of polar ice sheets and their impact on global environmental changes (sea level, ocean current cycle, atmospheric circulation, etc.). Mechanical rotary cutting is a widely used drilling method that drives the cutter to rotate to cut and drill through ice layers. It is necessary to conduct in-depth research on the brittle fracture behavior of ice and mechanical model and to analyze the factors and specific mechanisms (cutter's angle, rotation speed of the drill bit, and cutting depth) affecting cutting force for the rational design of ice core drill systems, improving the efficiency of ice core drilling and ensuring the drilling process runs smoothly. Therefore, in this paper, the process of ice cutting was observed, the fracture mechanics characteristics of the ice cutting process were analyzed, the formation process of ice chips was divided into three stages, and a mathematical model for the cutting force was established based on the observation results. The paper describes the damage conditions of ice failure and points out the factors and specific laws influencing cutting force. Furthermore, the cutting force generated under various experimental conditions was tested. Based on specified real-time variation curves of cutting force, the characteristics of cutting force were analyzed during the cutting and drilling process. Based on comparison to results of the average cutting force, the influence mechanism of various parameters acting on the cutting force was obtained. This proves the correctness of the mathematical model of the cutting force and provides a theoretical reference for the calculation of the cutting force during ice cutting and drilling in polar regions.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"14 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808966","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. Despite decades of effort, there remains an inability to measure snow water equivalent (SWE) at high spatial resolutions using remote sensing. Passive gamma ray spectrometry is one of the only well-established methods to reliably remotely sense SWE, but airborne applications to date have been limited to observing kilometre-scale areal averages. Noting the increasing capabilities of unoccupied aerial vehicles (UAVs) and miniaturization of passive gamma ray spectrometers, this study tested the ability of a UAV-borne gamma spectrometer and concomitant UAV-borne lidar to quantify the spatial variability of SWE at high spatial resolutions. Gamma and lidar observations from a UAV (UAV-gamma and UAV-lidar) were collected over two seasons from shallow, wind-blown, prairie snowpacks in Saskatchewan, Canada, with validation data collected from manual snow depth and density observations. A fine-resolution (0.25 m) reference dataset of SWE, to test UAV-gamma methods, was developed from UAV-lidar snow depth and snow survey snow density observations. The ability of UAV-gamma to resolve the areal average and spatial variability of SWE was promising with appropriate flight characteristics. Survey flights flown at a velocity of 5 m s−1, altitude of 15 m, and line spacing of 15 m were unable to capture the average or spatial variability of SWE within the uncertainty of the reference dataset. Slower, lower, and denser flight lines at a velocity of 4 m s−1, altitude of 8 m, and line spacing of 8 m were able to successfully observe areal average SWE and its variability at spatial resolutions greater than 22.5 m. Using a combination of UAV-based gamma SWE and UAV-based lidar snow depth improved the spatial representation of SWE substantially and permitted estimation of SWE at a spatial resolution 0.25 m with a ± 14.3 mm error relative to the reference SWE dataset. UAV-borne gamma spectrometry to estimate SWE is a promising and novel technique that has the potential to improve the measurement of shallow prairie snowpacks, and when combined with UAV-borne lidar snow depths, can provide fine-resolution, high-accuracy estimates of prairie SWE. Research on optimal hardware, data processing, and interpolation techniques is called for to further improve this remote sensing product and explore its application in other environments.
摘要。尽管经过几十年的努力，仍无法利用遥感技术测量高空间分辨率的雪水当量（SWE）。被动伽马射线光谱仪是可靠遥感雪水当量的唯一成熟方法之一，但迄今为止机载应用仅限于观测千米级的平均面积。考虑到无人飞行器（UAV）能力的不断提高和被动伽马射线光谱仪的小型化，本研究测试了无人飞行器搭载的伽马光谱仪和无人飞行器搭载的激光雷达在高空间分辨率下量化 SWE 空间变化的能力。通过无人机（无人机-伽马和无人机-激光雷达）对加拿大萨斯喀彻温省被风吹起的浅层草原积雪进行了两季伽马和激光雷达观测，并通过人工雪深和密度观测收集了验证数据。根据无人机激光雷达雪深和雪地勘测雪密度观测数据，建立了 SWE 的精细分辨率（0.25 米）参考数据集，用于测试无人机伽马方法。在具备适当飞行特性的情况下，UAV-gamma 分辨 SWE 的区域平均值和空间变化的能力很有希望。以 5 米/秒-1 的速度、15 米的高度和 15 米的线间距进行的勘测飞行无法在参考数据集的不确定性范围内捕捉到 SWE 的平均值或空间变化。速度为 4 m s-1、高度为 8 m、线间距为 8 m 的飞行线路速度更慢、高度更低、密度更大，能够成功观测到平均 SWE 值及其空间分辨率大于 22.5 m 的变化。使用无人机伽马SWE和无人机激光雷达雪深相结合的方法，大大提高了SWE的空间表示能力，可以估算出空间分辨率为0.25米的SWE，与参考SWE数据集相比，误差为±14.3毫米。无人机载伽马能谱仪估算SWE是一项很有前途的新技术，有可能改进草原浅积雪的测量，如果与无人机载激光雷达雪深相结合，可以提供精细分辨率、高精度的草原SWE估算值。需要对最佳硬件、数据处理和插值技术进行研究，以进一步改进这种遥感产品，并探索其在其他环境中的应用。
{"title":"Measuring prairie snow water equivalent with combined UAV-borne gamma spectrometry and lidar","authors":"P. Harder, W. Helgason, John W. Pomeroy","doi":"10.5194/tc-18-3277-2024","DOIUrl":"https://doi.org/10.5194/tc-18-3277-2024","url":null,"abstract":"Abstract. Despite decades of effort, there remains an inability to measure snow water equivalent (SWE) at high spatial resolutions using remote sensing. Passive gamma ray spectrometry is one of the only well-established methods to reliably remotely sense SWE, but airborne applications to date have been limited to observing kilometre-scale areal averages. Noting the increasing capabilities of unoccupied aerial vehicles (UAVs) and miniaturization of passive gamma ray spectrometers, this study tested the ability of a UAV-borne gamma spectrometer and concomitant UAV-borne lidar to quantify the spatial variability of SWE at high spatial resolutions. Gamma and lidar observations from a UAV (UAV-gamma and UAV-lidar) were collected over two seasons from shallow, wind-blown, prairie snowpacks in Saskatchewan, Canada, with validation data collected from manual snow depth and density observations. A fine-resolution (0.25 m) reference dataset of SWE, to test UAV-gamma methods, was developed from UAV-lidar snow depth and snow survey snow density observations. The ability of UAV-gamma to resolve the areal average and spatial variability of SWE was promising with appropriate flight characteristics. Survey flights flown at a velocity of 5 m s−1, altitude of 15 m, and line spacing of 15 m were unable to capture the average or spatial variability of SWE within the uncertainty of the reference dataset. Slower, lower, and denser flight lines at a velocity of 4 m s−1, altitude of 8 m, and line spacing of 8 m were able to successfully observe areal average SWE and its variability at spatial resolutions greater than 22.5 m. Using a combination of UAV-based gamma SWE and UAV-based lidar snow depth improved the spatial representation of SWE substantially and permitted estimation of SWE at a spatial resolution 0.25 m with a ± 14.3 mm error relative to the reference SWE dataset. UAV-borne gamma spectrometry to estimate SWE is a promising and novel technique that has the potential to improve the measurement of shallow prairie snowpacks, and when combined with UAV-borne lidar snow depths, can provide fine-resolution, high-accuracy estimates of prairie SWE. Research on optimal hardware, data processing, and interpolation techniques is called for to further improve this remote sensing product and explore its application in other environments.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"121 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141812060","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}
N. Laxague, C. Zappa, Andrew R. Mahoney, J. Goodwin, C. Harris, Robert E. Schaeffer, R. Schaeffer Sr., S. Betcher, Donna D. W. Hauser, C. Witte, Jessica M. Lindsay, Ajit Subramaniam, Kate E. Turner, Alex Whiting
Abstract. In polar regions, sea ice is a crucial mediator of the interaction between Earth's atmosphere and oceans. Its formation and breakup is intimately connected with large-scale climatic processes, local weather patterns, and the use of sea ice in coastal Arctic regions by Indigenous people. In order to investigate the physical phenomena at the heart of this process, a set of targeted, intensive observations were made over spring sea ice melt and breakup in Kotzebue Sound, Alaska. These observations were planned and executed through a collaborative effort in which an Indigenous Elder advisory council from Kotzebue and scientists participated in co-production of hypotheses and observational research, including a stronger understanding of the physical properties of sea ice during spring melt. Here we present the results of observations performed using high-endurance, fixed-wing uncrewed aerial vehicles (UAVs) containing custom-built scientific payloads. Repeated flights over the measurement period captured the early stages of the transition from a white, snow-covered state to a broken-up, bare blue-green state. We found that the reflectance of sea ice features depends strongly on their size. Snow patches get darker as they get smaller, an effect owed to the geometric relationship between the bright interior and the darker, melting feature edges. Conversely, bare patches get darker as they get larger. For the largest ice features observed, bare blue-green ice patches were found to be ≈ 20 % less reflective than average across all observational cases, while large snowy white ice patches were found to be ≈ 20 % more reflective than that same average.
{"title":"The radiative and geometric properties of melting first-year landfast sea ice in the Arctic","authors":"N. Laxague, C. Zappa, Andrew R. Mahoney, J. Goodwin, C. Harris, Robert E. Schaeffer, R. Schaeffer Sr., S. Betcher, Donna D. W. Hauser, C. Witte, Jessica M. Lindsay, Ajit Subramaniam, Kate E. Turner, Alex Whiting","doi":"10.5194/tc-18-3297-2024","DOIUrl":"https://doi.org/10.5194/tc-18-3297-2024","url":null,"abstract":"Abstract. In polar regions, sea ice is a crucial mediator of the interaction between Earth's atmosphere and oceans. Its formation and breakup is intimately connected with large-scale climatic processes, local weather patterns, and the use of sea ice in coastal Arctic regions by Indigenous people. In order to investigate the physical phenomena at the heart of this process, a set of targeted, intensive observations were made over spring sea ice melt and breakup in Kotzebue Sound, Alaska. These observations were planned and executed through a collaborative effort in which an Indigenous Elder advisory council from Kotzebue and scientists participated in co-production of hypotheses and observational research, including a stronger understanding of the physical properties of sea ice during spring melt. Here we present the results of observations performed using high-endurance, fixed-wing uncrewed aerial vehicles (UAVs) containing custom-built scientific payloads. Repeated flights over the measurement period captured the early stages of the transition from a white, snow-covered state to a broken-up, bare blue-green state. We found that the reflectance of sea ice features depends strongly on their size. Snow patches get darker as they get smaller, an effect owed to the geometric relationship between the bright interior and the darker, melting feature edges. Conversely, bare patches get darker as they get larger. For the largest ice features observed, bare blue-green ice patches were found to be ≈ 20 % less reflective than average across all observational cases, while large snowy white ice patches were found to be ≈ 20 % more reflective than that same average.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"72 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810406","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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