Andrew N. Hennig, D. Mucciarone, Stanley S. Jacobs, R. Mortlock, Robert B. Dunbar
Abstract. Ice sheet mass loss from Antarctica is greatest in the Amundsen Sea sector, where “warm” modified Circumpolar Deep Water moves onto the continental shelf and melts and thins the bases of ice shelves hundreds of meters below the sea surface. We use nearly 1000 paired salinity and oxygen isotope analyses of seawater samples collected on seven expeditions from 1994 to 2020 to produce a time series of glacial meltwater inventory for the southeastern Amundsen Sea continental shelf. Deep water column salinity–δ18O relationships yield freshwater end-member δ18O values from -31.3±1.0‰ to -28.4±1.0‰, consistent with the isotopic composition of local glacial ice. We use a two-component meteoric water end-member approach that accounts for precipitation in the upper water column, and a pure glacial meteoric water end-member is employed for the deep water column. Meteoric water inventories are comprised of nearly pure glacial meltwater in deep shelf waters and of >74 % glacial meltwater in the upper water column. Total meteoric water inventories range from 8.1±0.7 to 9.6±0.8 m and exhibit greater interannual variability than trend over the study period, based on the available data. The relatively long residence time in the southeastern Amundsen Sea allows changes in mean meteoric water inventories to diagnose large changes in local melt rates, and improved understanding of regional circulation could produce well-constrained glacial meltwater fluxes. The two-component meteoric end-member technique improves the accuracy of the sea ice melt and meteoric fractions estimated from seawater δ18O measurements throughout the entire water column and increases the utility for the broader application of these estimates.
{"title":"Meteoric water and glacial melt in the southeastern Amundsen Sea: a time series from 1994 to 2020","authors":"Andrew N. Hennig, D. Mucciarone, Stanley S. Jacobs, R. Mortlock, Robert B. Dunbar","doi":"10.5194/tc-18-791-2024","DOIUrl":"https://doi.org/10.5194/tc-18-791-2024","url":null,"abstract":"Abstract. Ice sheet mass loss from Antarctica is greatest in the Amundsen Sea sector, where “warm” modified Circumpolar Deep Water moves onto the continental shelf and melts and thins the bases of ice shelves hundreds of meters below the sea surface. We use nearly 1000 paired salinity and oxygen isotope analyses of seawater samples collected on seven expeditions from 1994 to 2020 to produce a time series of glacial meltwater inventory for the southeastern Amundsen Sea continental shelf. Deep water column salinity–δ18O relationships yield freshwater end-member δ18O values from -31.3±1.0‰ to -28.4±1.0‰, consistent with the isotopic composition of local glacial ice. We use a two-component meteoric water end-member approach that accounts for precipitation in the upper water column, and a pure glacial meteoric water end-member is employed for the deep water column. Meteoric water inventories are comprised of nearly pure glacial meltwater in deep shelf waters and of >74 % glacial meltwater in the upper water column. Total meteoric water inventories range from 8.1±0.7 to 9.6±0.8 m and exhibit greater interannual variability than trend over the study period, based on the available data. The relatively long residence time in the southeastern Amundsen Sea allows changes in mean meteoric water inventories to diagnose large changes in local melt rates, and improved understanding of regional circulation could produce well-constrained glacial meltwater fluxes. The two-component meteoric end-member technique improves the accuracy of the sea ice melt and meteoric fractions estimated from seawater δ18O measurements throughout the entire water column and increases the utility for the broader application of these estimates.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"208 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140447491","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. Debris-covered glaciers exist in many mountain ranges and play an important role in the regional water cycle. However, modelling the surface mass balance, runoff contribution and future evolution of debris-covered glaciers is fraught with uncertainty as accurate observations on small-scale variations in debris thickness and sub-debris ice melt rates are only available for a few locations worldwide. Here we describe a customised low-cost unoccupied aerial vehicle (UAV) for high-resolution thermal imaging of mountain glaciers and present a complete open-source pipeline that facilitates the generation of accurate surface temperature and debris thickness maps from radiometric images. First, a radiometric orthophoto is computed from individual radiometric UAV images using structure-from-motion and multi-view-stereo techniques. User-specific calibration and correction procedures can then be applied to the radiometric orthophoto to account for atmospheric and environmental influences that affect the radiometric measurement. The thermal orthophoto reveals distinct spatial variations in surface temperature across the surveyed debris-covered area. Finally, a high-resolution debris thickness map is derived from the corrected thermal orthophoto using an empirical or inverse surface energy balance model that relates surface temperature to debris thickness and is calibrated against in situ measurements. Our results from a small-scale experiment on the Kanderfirn (also known as Kander Neve) in the Swiss Alps show that the surface temperature and thickness of a relatively thin debris layer (ca. 0–15 cm) can be mapped with high accuracy using an empirical or physical model. On snow and ice surfaces, the mean deviation of the mapped surface temperature from the melting point (∼ 0 ∘C) was 0.6 ± 2.0 ∘C. The root-mean-square error of the modelled debris thickness was 1.3 cm. Through the detailed mapping, typical small-scale debris features and debris thickness patterns become visible, which are not spatially resolved by the thermal infrared sensors of current-generation satellites. The presented approach paves the way for comprehensive high-resolution supraglacial debris thickness mapping and opens up new opportunities for more accurate monitoring and modelling of debris-covered glaciers.
{"title":"A low-cost and open-source approach for supraglacial debris thickness mapping using UAV-based infrared thermography","authors":"J. Messmer, A. Groos","doi":"10.5194/tc-18-719-2024","DOIUrl":"https://doi.org/10.5194/tc-18-719-2024","url":null,"abstract":"Abstract. Debris-covered glaciers exist in many mountain ranges and play an important role in the regional water cycle. However, modelling the surface mass balance, runoff contribution and future evolution of debris-covered glaciers is fraught with uncertainty as accurate observations on small-scale variations in debris thickness and sub-debris ice melt rates are only available for a few locations worldwide. Here we describe a customised low-cost unoccupied aerial vehicle (UAV) for high-resolution thermal imaging of mountain glaciers and present a complete open-source pipeline that facilitates the generation of accurate surface temperature and debris thickness maps from radiometric images. First, a radiometric orthophoto is computed from individual radiometric UAV images using structure-from-motion and multi-view-stereo techniques. User-specific calibration and correction procedures can then be applied to the radiometric orthophoto to account for atmospheric and environmental influences that affect the radiometric measurement. The thermal orthophoto reveals distinct spatial variations in surface temperature across the surveyed debris-covered area. Finally, a high-resolution debris thickness map is derived from the corrected thermal orthophoto using an empirical or inverse surface energy balance model that relates surface temperature to debris thickness and is calibrated against in situ measurements. Our results from a small-scale experiment on the Kanderfirn (also known as Kander Neve) in the Swiss Alps show that the surface temperature and thickness of a relatively thin debris layer (ca. 0–15 cm) can be mapped with high accuracy using an empirical or physical model. On snow and ice surfaces, the mean deviation of the mapped surface temperature from the melting point (∼ 0 ∘C) was 0.6 ± 2.0 ∘C. The root-mean-square error of the modelled debris thickness was 1.3 cm. Through the detailed mapping, typical small-scale debris features and debris thickness patterns become visible, which are not spatially resolved by the thermal infrared sensors of current-generation satellites. The presented approach paves the way for comprehensive high-resolution supraglacial debris thickness mapping and opens up new opportunities for more accurate monitoring and modelling of debris-covered glaciers.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"28 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140450429","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}
Qinggang Gao, L. Sime, Alison J. McLaren, T. Bracegirdle, Emilie Capron, Rachael H. Rhodes, H. Steen‐Larsen, Xiaoxu Shi, Martin Werner
Abstract. Improving our understanding of the controls on Antarctic precipitation is critical for gaining insights into past and future polar and global environmental changes. Here we develop innovative water tracing diagnostics in the atmospheric general circulation model ECHAM6. These tracers provide new detailed information on moisture source locations and properties of Antarctic precipitation. In the preindustrial simulation, annual mean Antarctic precipitation originating from the open ocean has a source latitude range of 49–35∘ S, a source sea surface temperature range of 9.8–16.3 ∘C, a source 2 m relative humidity range of 75.6 %–83.3 %, and a source 10 m wind velocity (vel10) range of 10.1 to 11.3 m s−1. These results are consistent with estimates from existing literature. Central Antarctic precipitation is sourced from more equatorward (distant) sources via elevated transport pathways compared to coastal Antarctic precipitation. This has been attributed to a moist isentropic framework; i.e. poleward vapour transport tends to follow constant equivalent potential temperature. However, we find notable deviations from this tendency especially in the lower troposphere, likely due to radiative cooling. Heavy precipitation is sourced by longer-range moisture transport: it comes from 2.9∘ (300 km, averaged over Antarctica) more equatorward (distant) sources compared to the rest of precipitation. Precipitation during negative phases of the Southern Annular Mode (SAM) also comes from more equatorward moisture sources (by 2.4∘, averaged over Antarctica) compared to precipitation during positive SAM phases, likely due to amplified planetary waves during negative SAM phases. Moreover, source vel10 of annual mean precipitation is on average 2.1 m s−1 higher than annual mean vel10 at moisture source locations from which the precipitation originates. This shows that the evaporation of moisture driving Antarctic precipitation occurs under windier conditions than average. We quantified this dynamic control of Southern Ocean surface wind on moisture availability for Antarctic precipitation. Overall, the innovative water tracing diagnostics enhance our understanding of the controlling factors of Antarctic precipitation.
{"title":"Evaporative controls on Antarctic precipitation: an ECHAM6 model study using innovative water tracer diagnostics","authors":"Qinggang Gao, L. Sime, Alison J. McLaren, T. Bracegirdle, Emilie Capron, Rachael H. Rhodes, H. Steen‐Larsen, Xiaoxu Shi, Martin Werner","doi":"10.5194/tc-18-683-2024","DOIUrl":"https://doi.org/10.5194/tc-18-683-2024","url":null,"abstract":"Abstract. Improving our understanding of the controls on Antarctic precipitation is critical for gaining insights into past and future polar and global environmental changes. Here we develop innovative water tracing diagnostics in the atmospheric general circulation model ECHAM6. These tracers provide new detailed information on moisture source locations and properties of Antarctic precipitation. In the preindustrial simulation, annual mean Antarctic precipitation originating from the open ocean has a source latitude range of 49–35∘ S, a source sea surface temperature range of 9.8–16.3 ∘C, a source 2 m relative humidity range of 75.6 %–83.3 %, and a source 10 m wind velocity (vel10) range of 10.1 to 11.3 m s−1. These results are consistent with estimates from existing literature. Central Antarctic precipitation is sourced from more equatorward (distant) sources via elevated transport pathways compared to coastal Antarctic precipitation. This has been attributed to a moist isentropic framework; i.e. poleward vapour transport tends to follow constant equivalent potential temperature. However, we find notable deviations from this tendency especially in the lower troposphere, likely due to radiative cooling. Heavy precipitation is sourced by longer-range moisture transport: it comes from 2.9∘ (300 km, averaged over Antarctica) more equatorward (distant) sources compared to the rest of precipitation. Precipitation during negative phases of the Southern Annular Mode (SAM) also comes from more equatorward moisture sources (by 2.4∘, averaged over Antarctica) compared to precipitation during positive SAM phases, likely due to amplified planetary waves during negative SAM phases. Moreover, source vel10 of annual mean precipitation is on average 2.1 m s−1 higher than annual mean vel10 at moisture source locations from which the precipitation originates. This shows that the evaporation of moisture driving Antarctic precipitation occurs under windier conditions than average. We quantified this dynamic control of Southern Ocean surface wind on moisture availability for Antarctic precipitation. Overall, the innovative water tracing diagnostics enhance our understanding of the controlling factors of Antarctic precipitation.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"97 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139781314","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}
Qinggang Gao, L. Sime, Alison J. McLaren, T. Bracegirdle, Emilie Capron, Rachael H. Rhodes, H. Steen‐Larsen, Xiaoxu Shi, Martin Werner
Abstract. Improving our understanding of the controls on Antarctic precipitation is critical for gaining insights into past and future polar and global environmental changes. Here we develop innovative water tracing diagnostics in the atmospheric general circulation model ECHAM6. These tracers provide new detailed information on moisture source locations and properties of Antarctic precipitation. In the preindustrial simulation, annual mean Antarctic precipitation originating from the open ocean has a source latitude range of 49–35∘ S, a source sea surface temperature range of 9.8–16.3 ∘C, a source 2 m relative humidity range of 75.6 %–83.3 %, and a source 10 m wind velocity (vel10) range of 10.1 to 11.3 m s−1. These results are consistent with estimates from existing literature. Central Antarctic precipitation is sourced from more equatorward (distant) sources via elevated transport pathways compared to coastal Antarctic precipitation. This has been attributed to a moist isentropic framework; i.e. poleward vapour transport tends to follow constant equivalent potential temperature. However, we find notable deviations from this tendency especially in the lower troposphere, likely due to radiative cooling. Heavy precipitation is sourced by longer-range moisture transport: it comes from 2.9∘ (300 km, averaged over Antarctica) more equatorward (distant) sources compared to the rest of precipitation. Precipitation during negative phases of the Southern Annular Mode (SAM) also comes from more equatorward moisture sources (by 2.4∘, averaged over Antarctica) compared to precipitation during positive SAM phases, likely due to amplified planetary waves during negative SAM phases. Moreover, source vel10 of annual mean precipitation is on average 2.1 m s−1 higher than annual mean vel10 at moisture source locations from which the precipitation originates. This shows that the evaporation of moisture driving Antarctic precipitation occurs under windier conditions than average. We quantified this dynamic control of Southern Ocean surface wind on moisture availability for Antarctic precipitation. Overall, the innovative water tracing diagnostics enhance our understanding of the controlling factors of Antarctic precipitation.
{"title":"Evaporative controls on Antarctic precipitation: an ECHAM6 model study using innovative water tracer diagnostics","authors":"Qinggang Gao, L. Sime, Alison J. McLaren, T. Bracegirdle, Emilie Capron, Rachael H. Rhodes, H. Steen‐Larsen, Xiaoxu Shi, Martin Werner","doi":"10.5194/tc-18-683-2024","DOIUrl":"https://doi.org/10.5194/tc-18-683-2024","url":null,"abstract":"Abstract. Improving our understanding of the controls on Antarctic precipitation is critical for gaining insights into past and future polar and global environmental changes. Here we develop innovative water tracing diagnostics in the atmospheric general circulation model ECHAM6. These tracers provide new detailed information on moisture source locations and properties of Antarctic precipitation. In the preindustrial simulation, annual mean Antarctic precipitation originating from the open ocean has a source latitude range of 49–35∘ S, a source sea surface temperature range of 9.8–16.3 ∘C, a source 2 m relative humidity range of 75.6 %–83.3 %, and a source 10 m wind velocity (vel10) range of 10.1 to 11.3 m s−1. These results are consistent with estimates from existing literature. Central Antarctic precipitation is sourced from more equatorward (distant) sources via elevated transport pathways compared to coastal Antarctic precipitation. This has been attributed to a moist isentropic framework; i.e. poleward vapour transport tends to follow constant equivalent potential temperature. However, we find notable deviations from this tendency especially in the lower troposphere, likely due to radiative cooling. Heavy precipitation is sourced by longer-range moisture transport: it comes from 2.9∘ (300 km, averaged over Antarctica) more equatorward (distant) sources compared to the rest of precipitation. Precipitation during negative phases of the Southern Annular Mode (SAM) also comes from more equatorward moisture sources (by 2.4∘, averaged over Antarctica) compared to precipitation during positive SAM phases, likely due to amplified planetary waves during negative SAM phases. Moreover, source vel10 of annual mean precipitation is on average 2.1 m s−1 higher than annual mean vel10 at moisture source locations from which the precipitation originates. This shows that the evaporation of moisture driving Antarctic precipitation occurs under windier conditions than average. We quantified this dynamic control of Southern Ocean surface wind on moisture availability for Antarctic precipitation. Overall, the innovative water tracing diagnostics enhance our understanding of the controlling factors of Antarctic precipitation.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"27 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139841101","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}
Zachary Hoppinen, S. Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, C. Vuyovich
Abstract. This study evaluates using interferometry on low-frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 d temporal baseline, captured by an L-band aerial platform, NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved measurements shows a strong Pearson correlation (R=0.80) and low RMSE (0.1 m, n=64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R=0.52, n=57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R=0.60, RMSD = 0.023 m, n=3.2×106) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R=0.72, RMSD = 0.013 m, n=6.5×104). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.
摘要。本研究评估了在低频合成孔径雷达（SAR）图像上使用干涉测量法监测季节和天气尺度的雪水当量（SWE）的情况。作为 NASA SnowEx 2020 年和 2021 年活动的一部分，我们从爱达荷州中部上空由 L 波段航空平台（NASA 无人驾驶飞行器合成孔径雷达 (UAVSAR)）捕获的九对 SAR 图像（平均 8 天时间基线）中检索了 SWE 变化。将检索到的 SWE 变化与重合的原地测量值（SNOTEL 和 SnowEx 野外作业中的雪坑）以及 100 米网格化 SnowModel 模拟的 SWE 变化进行了比较。原位测量值与检索测量值的比较结果显示，雪深变化的皮尔逊相关性强（R=0.80），均方根误差小（0.1 米，n=64），而 SWE 变化的结果类似（均方根误差 = 0.04 米，R=0.52，n=57）。检索到的 SWE 变化与 SnowModel SWE 变化之间的比较也显示出良好的相关性（R=0.60，RMSD = 0.023 m，n=3.2×106），尤其是对于没有模型融化和树木覆盖率低的像素子集，相关性更高（R=0.72，RMSD = 0.013 m，n=6.5×104）。最后，我们对各种因素的检索结果进行了分类，结果表明，在海拔较低、入射角度较高、树木比例和高度较高以及累积融化量较大的情况下，建模值与检索值之间的相关性降低。本研究在以往干涉测量工作的基础上，利用大空间范围内整个冬季的 L 波段合成孔径雷达图像时间序列，对照原地和模拟结果，评估了 SWE 变化检索的准确性，以及检索准确性的控制因素。
{"title":"Snow water equivalent retrieval over Idaho – Part 2: Using L-band UAVSAR repeat-pass interferometry","authors":"Zachary Hoppinen, S. Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, C. Vuyovich","doi":"10.5194/tc-18-575-2024","DOIUrl":"https://doi.org/10.5194/tc-18-575-2024","url":null,"abstract":"Abstract. This study evaluates using interferometry on low-frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 d temporal baseline, captured by an L-band aerial platform, NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved measurements shows a strong Pearson correlation (R=0.80) and low RMSE (0.1 m, n=64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R=0.52, n=57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R=0.60, RMSD = 0.023 m, n=3.2×106) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R=0.72, RMSD = 0.013 m, n=6.5×104). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"57 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139784752","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. Vandecrux, R. Fausto, J. Box, F. Covi, R. Hock, Å. Rennermalm, A. Heilig, Jakob Abermann, D. van As, E. Bjerre, X. Fettweis, P. Smeets, P. Kuipers Munneke, M. R. van den Broeke, M. Brils, P. Langen, R. Mottram, A. Ahlstrøm
Abstract. Surface melt on the Greenland ice sheet has been increasing in intensity and extent over the last decades due to Arctic atmospheric warming. Surface melt depends on the surface energy balance, which includes the atmospheric forcing but also the thermal budget of the snow, firn and ice near the ice sheet surface. The temperature of the ice sheet subsurface has been used as an indicator of the thermal state of the ice sheet's surface. Here, we present a compilation of 4612 measurements of firn and ice temperature at 10 m below the surface (T10 m) across the ice sheet, spanning from 1912 to 2022. The measurements are either instantaneous or monthly averages. We train an artificial neural network model (ANN) on 4597 of these point observations, weighted by their relative representativity, and use it to reconstruct T10 m over the entire Greenland ice sheet for the period 1950–2022 at a monthly timescale. We use 10-year averages and mean annual values of air temperature and snowfall from the ERA5 reanalysis dataset as model input. The ANN indicates a Greenland-wide positive trend of T10 m at 0.2 ∘C per decade during the 1950–2022 period, with a cooling during 1950–1985 (−0.4 ∘C per decade) followed by a warming during 1985–2022 (+0.7 ∘ per decade). Regional climate models HIRHAM5, RACMO2.3p2 and MARv3.12 show mixed results compared to the observational T10 m dataset, with mean differences ranging from −0.4 ∘C (HIRHAM) to 1.2 ∘C (MAR) and root mean squared differences ranging from 2.8 ∘C (HIRHAM) to 4.7 ∘C (MAR). The observation-based ANN also reveals an underestimation of the subsurface warming trends in climate models for the bare-ice and dry-snow areas. The subsurface warming brings the Greenland ice sheet surface closer to the melting point, reducing the amount of energy input required for melting. Our compilation documents the response of the ice sheet subsurface to atmospheric warming and will enable further improvements of models used for ice sheet mass loss assessment and reduce the uncertainty in projections.
{"title":"Recent warming trends of the Greenland ice sheet documented by historical firn and ice temperature observations and machine learning","authors":"B. Vandecrux, R. Fausto, J. Box, F. Covi, R. Hock, Å. Rennermalm, A. Heilig, Jakob Abermann, D. van As, E. Bjerre, X. Fettweis, P. Smeets, P. Kuipers Munneke, M. R. van den Broeke, M. Brils, P. Langen, R. Mottram, A. Ahlstrøm","doi":"10.5194/tc-18-609-2024","DOIUrl":"https://doi.org/10.5194/tc-18-609-2024","url":null,"abstract":"Abstract. Surface melt on the Greenland ice sheet has been increasing in intensity and extent over the last decades due to Arctic atmospheric warming. Surface melt depends on the surface energy balance, which includes the atmospheric forcing but also the thermal budget of the snow, firn and ice near the ice sheet surface. The temperature of the ice sheet subsurface has been used as an indicator of the thermal state of the ice sheet's surface. Here, we present a compilation of 4612 measurements of firn and ice temperature at 10 m below the surface (T10 m) across the ice sheet, spanning from 1912 to 2022. The measurements are either instantaneous or monthly averages. We train an artificial neural network model (ANN) on 4597 of these point observations, weighted by their relative representativity, and use it to reconstruct T10 m over the entire Greenland ice sheet for the period 1950–2022 at a monthly timescale. We use 10-year averages and mean annual values of air temperature and snowfall from the ERA5 reanalysis dataset as model input. The ANN indicates a Greenland-wide positive trend of T10 m at 0.2 ∘C per decade during the 1950–2022 period, with a cooling during 1950–1985 (−0.4 ∘C per decade) followed by a warming during 1985–2022 (+0.7 ∘ per decade). Regional climate models HIRHAM5, RACMO2.3p2 and MARv3.12 show mixed results compared to the observational T10 m dataset, with mean differences ranging from −0.4 ∘C (HIRHAM) to 1.2 ∘C (MAR) and root mean squared differences ranging from 2.8 ∘C (HIRHAM) to 4.7 ∘C (MAR). The observation-based ANN also reveals an underestimation of the subsurface warming trends in climate models for the bare-ice and dry-snow areas. The subsurface warming brings the Greenland ice sheet surface closer to the melting point, reducing the amount of energy input required for melting. Our compilation documents the response of the ice sheet subsurface to atmospheric warming and will enable further improvements of models used for ice sheet mass loss assessment and reduce the uncertainty in projections.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"360 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139842780","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}
Claudio Stefanini, G. Macelloni, M. Leduc-Leballeur, Vincent Favier, Benjamin Pohl, G. Picard
Abstract. This study explores the seasonal variations in snow grain size on the East Antarctic Plateau, where dry metamorphism occurs, by using microwave radiometer observations from 2000 to 2022. Local meteorological conditions and large-scale atmospheric phenomena have been considered in order to explain some peculiar changes in the snow grains. We find that the highest ice divide is the region with the largest grain size in the summer, mainly because the wind speed is low. Moreover, some extreme grain size values with respect to the average (over +3σ) were identified. In these cases, the ERA5 reanalysis revealed a high-pressure blocking close to the onsets of the summer increase in the grain size. It channels moisture intrusions from the mid-latitudes, through atmospheric rivers that cause major snowfall events over the plateau. If conditions of weak wind and low temperature occur during the following weeks, dry snow metamorphism is facilitated, leading to grain growth. This determines anomalous high maximums of the snow grain size at the end of summer. These phenomena confirm the importance of moisture intrusion events in East Antarctica and their impact on the physical properties of the ice sheet surface, with a co-occurrence of atmospheric rivers and seasonal changes in the grain size with a significance of over 95 %.
{"title":"Extreme events of snow grain size increase in East Antarctica and their relationship with meteorological conditions","authors":"Claudio Stefanini, G. Macelloni, M. Leduc-Leballeur, Vincent Favier, Benjamin Pohl, G. Picard","doi":"10.5194/tc-18-593-2024","DOIUrl":"https://doi.org/10.5194/tc-18-593-2024","url":null,"abstract":"Abstract. This study explores the seasonal variations in snow grain size on the East Antarctic Plateau, where dry metamorphism occurs, by using microwave radiometer observations from 2000 to 2022. Local meteorological conditions and large-scale atmospheric phenomena have been considered in order to explain some peculiar changes in the snow grains. We find that the highest ice divide is the region with the largest grain size in the summer, mainly because the wind speed is low. Moreover, some extreme grain size values with respect to the average (over +3σ) were identified. In these cases, the ERA5 reanalysis revealed a high-pressure blocking close to the onsets of the summer increase in the grain size. It channels moisture intrusions from the mid-latitudes, through atmospheric rivers that cause major snowfall events over the plateau. If conditions of weak wind and low temperature occur during the following weeks, dry snow metamorphism is facilitated, leading to grain growth. This determines anomalous high maximums of the snow grain size at the end of summer. These phenomena confirm the importance of moisture intrusion events in East Antarctica and their impact on the physical properties of the ice sheet surface, with a co-occurrence of atmospheric rivers and seasonal changes in the grain size with a significance of over 95 %.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"74 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139843777","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}
S. Oveisgharan, Robert Zinke, Zachary Hoppinen, Hans Peter Marshall
Abstract. Snow water equivalent (SWE) is identified as the key element of the snowpack that impacts rivers' streamflow and water cycle. Both active and passive microwave remote sensing methods have been used to retrieve SWE, but there does not currently exist a SWE product that provides useful estimates in mountainous terrain. Active sensors provide higher-resolution observations, but the suitable radar frequencies and temporal repeat intervals have not been available until recently. Interferometric synthetic aperture radar (InSAR) has been shown to have the potential to estimate SWE change. In this study, we apply this technique to a long time series of 6 d temporal repeat Sentinel-1 C-band data from the 2020–2021 winter. The retrievals show statistically significant correlations both temporally and spatially with independent in situ measurements of SWE. The SWE change measurements vary between −5.3 and 9.4 cm over the entire time series and all the in situ stations. The Pearson correlation and RMSE between retrieved SWE change observations and in situ stations measurements are 0.8 and 0.93 cm, respectively. The total retrieved SWE in the entire 2020–2021 time series shows an SWE error of less than 2 cm for the nine in situ stations in the scene. Additionally, the retrieved SWE using Sentinel-1 data is well correlated with lidar snow depth data, with correlation of more than 0.47. Low temporal coherence is identified as the main reason for degrading the performance of SWE retrieval using InSAR data. We also show that the performance of the phase unwrapping algorithm degrades in regions with low temporal coherence. A higher frequency such as L-band improves the temporal coherence and SWE ambiguity. SWE retrieval using C-band Sentinel-1 data is shown to be successful, but faster revisit is required to avoid low temporal coherence. Global SWE retrieval using radar interferometry will have a great opportunity with the upcoming L-band 12 d repeat-pass NASA-ISRO Synthetic Aperture Radar (NISAR) data and the future 6 d repeat-pass Radar Observing System for Europe in L-band (ROSE-L) data.
摘要雪水当量（SWE）被认为是影响河流流量和水循环的关键雪层要素。主动式和被动式微波遥感方法都被用来检索雪水当量，但目前还没有一种雪水当量产品能为山区地形提供有用的估算。有源传感器可提供更高分辨率的观测数据，但直到最近才有合适的雷达频率和时间重复间隔。干涉合成孔径雷达 (InSAR) 已被证明具有估算 SWE 变化的潜力。在本研究中，我们将这一技术应用于 2020-2021 年冬季的 6 天时间重复哨兵-1 C 波段长时间序列数据。检索结果表明，无论从时间上还是从空间上，SWE 都与独立的原地测量值有显著的统计相关性。在整个时间序列和所有原位站中，SWE 变化测量值在 -5.3 到 9.4 厘米之间。检索到的 SWE 变化观测值与原位站测量值之间的皮尔逊相关性和均方根误差分别为 0.8 厘米和 0.93 厘米。在整个 2020-2021 年时间序列中，9 个原地站的 SWE 误差均小于 2 厘米。此外，使用哨兵 1 号数据检索的西南降水量与激光雷达雪深数据相关性良好，相关性超过 0.47。低时间一致性被认为是降低使用 InSAR 数据检索 SWE 性能的主要原因。我们还发现，在低时间相干性区域，相位解包算法的性能也会下降。更高的频率（如 L 波段）可改善时间一致性和 SWE 模糊性。使用 C 波段哨兵-1 数据进行 SWE 检索证明是成功的，但需要更快的重访以避免低时间相干性。即将发布的 L 波段 12 d 重复通量的 NASA-ISRO 合成孔径雷达（NISAR）数据和未来的 L 波段 6 d 重复通量的欧洲雷达观测系统（ROSE-L）数据将为利用雷达干涉测量进行全球 SWE 检索提供绝佳机会。
{"title":"Snow water equivalent retrieval over Idaho – Part 1: Using Sentinel-1 repeat-pass interferometry","authors":"S. Oveisgharan, Robert Zinke, Zachary Hoppinen, Hans Peter Marshall","doi":"10.5194/tc-18-559-2024","DOIUrl":"https://doi.org/10.5194/tc-18-559-2024","url":null,"abstract":"Abstract. Snow water equivalent (SWE) is identified as the key element of the snowpack that impacts rivers' streamflow and water cycle. Both active and passive microwave remote sensing methods have been used to retrieve SWE, but there does not currently exist a SWE product that provides useful estimates in mountainous terrain. Active sensors provide higher-resolution observations, but the suitable radar frequencies and temporal repeat intervals have not been available until recently. Interferometric synthetic aperture radar (InSAR) has been shown to have the potential to estimate SWE change. In this study, we apply this technique to a long time series of 6 d temporal repeat Sentinel-1 C-band data from the 2020–2021 winter. The retrievals show statistically significant correlations both temporally and spatially with independent in situ measurements of SWE. The SWE change measurements vary between −5.3 and 9.4 cm over the entire time series and all the in situ stations. The Pearson correlation and RMSE between retrieved SWE change observations and in situ stations measurements are 0.8 and 0.93 cm, respectively. The total retrieved SWE in the entire 2020–2021 time series shows an SWE error of less than 2 cm for the nine in situ stations in the scene. Additionally, the retrieved SWE using Sentinel-1 data is well correlated with lidar snow depth data, with correlation of more than 0.47. Low temporal coherence is identified as the main reason for degrading the performance of SWE retrieval using InSAR data. We also show that the performance of the phase unwrapping algorithm degrades in regions with low temporal coherence. A higher frequency such as L-band improves the temporal coherence and SWE ambiguity. SWE retrieval using C-band Sentinel-1 data is shown to be successful, but faster revisit is required to avoid low temporal coherence. Global SWE retrieval using radar interferometry will have a great opportunity with the upcoming L-band 12 d repeat-pass NASA-ISRO Synthetic Aperture Radar (NISAR) data and the future 6 d repeat-pass Radar Observing System for Europe in L-band (ROSE-L) data.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"13 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139783569","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. Vandecrux, R. Fausto, J. Box, F. Covi, R. Hock, Å. Rennermalm, A. Heilig, Jakob Abermann, D. van As, E. Bjerre, X. Fettweis, P. Smeets, P. Kuipers Munneke, M. R. van den Broeke, M. Brils, P. Langen, R. Mottram, A. Ahlstrøm
Abstract. Surface melt on the Greenland ice sheet has been increasing in intensity and extent over the last decades due to Arctic atmospheric warming. Surface melt depends on the surface energy balance, which includes the atmospheric forcing but also the thermal budget of the snow, firn and ice near the ice sheet surface. The temperature of the ice sheet subsurface has been used as an indicator of the thermal state of the ice sheet's surface. Here, we present a compilation of 4612 measurements of firn and ice temperature at 10 m below the surface (T10 m) across the ice sheet, spanning from 1912 to 2022. The measurements are either instantaneous or monthly averages. We train an artificial neural network model (ANN) on 4597 of these point observations, weighted by their relative representativity, and use it to reconstruct T10 m over the entire Greenland ice sheet for the period 1950–2022 at a monthly timescale. We use 10-year averages and mean annual values of air temperature and snowfall from the ERA5 reanalysis dataset as model input. The ANN indicates a Greenland-wide positive trend of T10 m at 0.2 ∘C per decade during the 1950–2022 period, with a cooling during 1950–1985 (−0.4 ∘C per decade) followed by a warming during 1985–2022 (+0.7 ∘ per decade). Regional climate models HIRHAM5, RACMO2.3p2 and MARv3.12 show mixed results compared to the observational T10 m dataset, with mean differences ranging from −0.4 ∘C (HIRHAM) to 1.2 ∘C (MAR) and root mean squared differences ranging from 2.8 ∘C (HIRHAM) to 4.7 ∘C (MAR). The observation-based ANN also reveals an underestimation of the subsurface warming trends in climate models for the bare-ice and dry-snow areas. The subsurface warming brings the Greenland ice sheet surface closer to the melting point, reducing the amount of energy input required for melting. Our compilation documents the response of the ice sheet subsurface to atmospheric warming and will enable further improvements of models used for ice sheet mass loss assessment and reduce the uncertainty in projections.
{"title":"Recent warming trends of the Greenland ice sheet documented by historical firn and ice temperature observations and machine learning","authors":"B. Vandecrux, R. Fausto, J. Box, F. Covi, R. Hock, Å. Rennermalm, A. Heilig, Jakob Abermann, D. van As, E. Bjerre, X. Fettweis, P. Smeets, P. Kuipers Munneke, M. R. van den Broeke, M. Brils, P. Langen, R. Mottram, A. Ahlstrøm","doi":"10.5194/tc-18-609-2024","DOIUrl":"https://doi.org/10.5194/tc-18-609-2024","url":null,"abstract":"Abstract. Surface melt on the Greenland ice sheet has been increasing in intensity and extent over the last decades due to Arctic atmospheric warming. Surface melt depends on the surface energy balance, which includes the atmospheric forcing but also the thermal budget of the snow, firn and ice near the ice sheet surface. The temperature of the ice sheet subsurface has been used as an indicator of the thermal state of the ice sheet's surface. Here, we present a compilation of 4612 measurements of firn and ice temperature at 10 m below the surface (T10 m) across the ice sheet, spanning from 1912 to 2022. The measurements are either instantaneous or monthly averages. We train an artificial neural network model (ANN) on 4597 of these point observations, weighted by their relative representativity, and use it to reconstruct T10 m over the entire Greenland ice sheet for the period 1950–2022 at a monthly timescale. We use 10-year averages and mean annual values of air temperature and snowfall from the ERA5 reanalysis dataset as model input. The ANN indicates a Greenland-wide positive trend of T10 m at 0.2 ∘C per decade during the 1950–2022 period, with a cooling during 1950–1985 (−0.4 ∘C per decade) followed by a warming during 1985–2022 (+0.7 ∘ per decade). Regional climate models HIRHAM5, RACMO2.3p2 and MARv3.12 show mixed results compared to the observational T10 m dataset, with mean differences ranging from −0.4 ∘C (HIRHAM) to 1.2 ∘C (MAR) and root mean squared differences ranging from 2.8 ∘C (HIRHAM) to 4.7 ∘C (MAR). The observation-based ANN also reveals an underestimation of the subsurface warming trends in climate models for the bare-ice and dry-snow areas. The subsurface warming brings the Greenland ice sheet surface closer to the melting point, reducing the amount of energy input required for melting. Our compilation documents the response of the ice sheet subsurface to atmospheric warming and will enable further improvements of models used for ice sheet mass loss assessment and reduce the uncertainty in projections.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"8 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139783081","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}
Violaine Coulon, A. Klose, C. Kittel, T. Edwards, Fiona Turner, R. Winkelmann, F. Pattyn
Abstract. We use an observationally calibrated ice-sheet model to investigate the future trajectory of the Antarctic ice sheet related to uncertainties in the future balance between sub-shelf melting and ice discharge, on the one hand, and the surface mass balance, on the other. Our ensemble of simulations, forced by a panel of climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), suggests that the ocean will be the primary driver of short-term Antarctic mass loss, initiating ice loss in West Antarctica already during this century. The atmosphere initially plays a mitigating role through increased snowfall, leading to an Antarctic contribution to global mean sea-level rise by 2100 of 6 (−8 to 15) cm under a low-emission scenario and 5.5 (−10 to 16) cm under a very high-emission scenario. However, under the very high-emission pathway, the influence of the atmosphere shifts beyond the end of the century, becoming an amplifying driver of mass loss as the ice sheet's surface mass balance decreases. We show that this transition occurs when Antarctic near-surface warming exceeds a critical threshold of +7.5 ∘C, at which the increase in surface runoff outweighs the increase in snow accumulation, a signal that is amplified by the melt–elevation feedback. Therefore, under the very high-emission scenario, oceanic and atmospheric drivers are projected to result in a complete collapse of the West Antarctic ice sheet along with significant grounding-line retreat in the marine basins of the East Antarctic ice sheet, leading to a median global mean sea-level rise of 2.75 (6.95) m by 2300 (3000). Under a more sustainable socio-economic pathway, we find that the Antarctic ice sheet may still contribute to a median global mean sea-level rise of 0.62 (1.85) m by 2300 (3000). However, the rate of sea-level rise is significantly reduced as mass loss is likely to remain confined to the Amundsen Sea Embayment, where present-day climate conditions seem sufficient to commit to a continuous retreat of Thwaites Glacier.
{"title":"Disentangling the drivers of future Antarctic ice loss with a historically calibrated ice-sheet model","authors":"Violaine Coulon, A. Klose, C. Kittel, T. Edwards, Fiona Turner, R. Winkelmann, F. Pattyn","doi":"10.5194/tc-18-653-2024","DOIUrl":"https://doi.org/10.5194/tc-18-653-2024","url":null,"abstract":"Abstract. We use an observationally calibrated ice-sheet model to investigate the future trajectory of the Antarctic ice sheet related to uncertainties in the future balance between sub-shelf melting and ice discharge, on the one hand, and the surface mass balance, on the other. Our ensemble of simulations, forced by a panel of climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), suggests that the ocean will be the primary driver of short-term Antarctic mass loss, initiating ice loss in West Antarctica already during this century. The atmosphere initially plays a mitigating role through increased snowfall, leading to an Antarctic contribution to global mean sea-level rise by 2100 of 6 (−8 to 15) cm under a low-emission scenario and 5.5 (−10 to 16) cm under a very high-emission scenario. However, under the very high-emission pathway, the influence of the atmosphere shifts beyond the end of the century, becoming an amplifying driver of mass loss as the ice sheet's surface mass balance decreases. We show that this transition occurs when Antarctic near-surface warming exceeds a critical threshold of +7.5 ∘C, at which the increase in surface runoff outweighs the increase in snow accumulation, a signal that is amplified by the melt–elevation feedback. Therefore, under the very high-emission scenario, oceanic and atmospheric drivers are projected to result in a complete collapse of the West Antarctic ice sheet along with significant grounding-line retreat in the marine basins of the East Antarctic ice sheet, leading to a median global mean sea-level rise of 2.75 (6.95) m by 2300 (3000). Under a more sustainable socio-economic pathway, we find that the Antarctic ice sheet may still contribute to a median global mean sea-level rise of 0.62 (1.85) m by 2300 (3000). However, the rate of sea-level rise is significantly reduced as mass loss is likely to remain confined to the Amundsen Sea Embayment, where present-day climate conditions seem sufficient to commit to a continuous retreat of Thwaites Glacier.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"74 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139843780","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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