Louise Sandberg Sørensen, R. Bahbah, S. Simonsen, Natalia Havelund Andersen, Jade S. Bowling, N. Gourmelen, Alexander J. Horton, N. Karlsson, A. Leeson, Jennifer Maddalena, M. McMillan, A. Solgaard, B. Wessel
Abstract. Subglacial lakes form beneath ice sheets and ice caps if water is available and if bedrock and surface topography are able to retain the water. On a regional scale, the lakes modulate the timing and rate of freshwater flow through the subglacial system to the ocean by acting as reservoirs. More than 100 hydrologically active subglacial lakes that drain and recharge periodically have been documented under the Antarctic Ice Sheet, while only approximately 20 active lakes have been identified in Greenland. Active lakes may be identified by local changes in ice topography caused by the drainage or recharge of the lake beneath the ice. The small size of the Greenlandic subglacial lakes puts additional demands on mapping capabilities to resolve the evolving surface topography in sufficient detail to record their temporal behaviour. Here, we explore the potential for using CryoSat-2 swath-processed data, together with TanDEM-X digital elevation models, to improve the monitoring capabilities of active subglacial lakes in Greenland. We focus on four subglacial lakes previously described in the literature and combine the data with ArcticDEMs to obtain improved measurements of the evolution of these four lakes. We find that with careful tuning of the swath processor and filtering of the output data, the inclusion of these data, together with the TanDEM-X data, provides important information on lake activity, documenting, for example, that the ice surface collapse basin on Flade Isblink Ice Cap was 50 % (30 m) deeper than previously recorded. We also present evidence of a new, active subglacial lake in southwestern Greenland, which is located close to an already known lake. Both lakes probably drained within 1 month in the summer of 2012, which suggests either that they are hydrologically connected or that the drainages were independently triggered by extensive surface melt. If the hydrological connection is confirmed, this would to our knowledge be the first indication of hydrologically connected subglacial lakes in Greenland.
{"title":"Improved monitoring of subglacial lake activity in Greenland","authors":"Louise Sandberg Sørensen, R. Bahbah, S. Simonsen, Natalia Havelund Andersen, Jade S. Bowling, N. Gourmelen, Alexander J. Horton, N. Karlsson, A. Leeson, Jennifer Maddalena, M. McMillan, A. Solgaard, B. Wessel","doi":"10.5194/tc-18-505-2024","DOIUrl":"https://doi.org/10.5194/tc-18-505-2024","url":null,"abstract":"Abstract. Subglacial lakes form beneath ice sheets and ice caps if water is available and if bedrock and surface topography are able to retain the water. On a regional scale, the lakes modulate the timing and rate of freshwater flow through the subglacial system to the ocean by acting as reservoirs. More than 100 hydrologically active subglacial lakes that drain and recharge periodically have been documented under the Antarctic Ice Sheet, while only approximately 20 active lakes have been identified in Greenland. Active lakes may be identified by local changes in ice topography caused by the drainage or recharge of the lake beneath the ice. The small size of the Greenlandic subglacial lakes puts additional demands on mapping capabilities to resolve the evolving surface topography in sufficient detail to record their temporal behaviour. Here, we explore the potential for using CryoSat-2 swath-processed data, together with TanDEM-X digital elevation models, to improve the monitoring capabilities of active subglacial lakes in Greenland. We focus on four subglacial lakes previously described in the literature and combine the data with ArcticDEMs to obtain improved measurements of the evolution of these four lakes. We find that with careful tuning of the swath processor and filtering of the output data, the inclusion of these data, together with the TanDEM-X data, provides important information on lake activity, documenting, for example, that the ice surface collapse basin on Flade Isblink Ice Cap was 50 % (30 m) deeper than previously recorded. We also present evidence of a new, active subglacial lake in southwestern Greenland, which is located close to an already known lake. Both lakes probably drained within 1 month in the summer of 2012, which suggests either that they are hydrologically connected or that the drainages were independently triggered by extensive surface melt. If the hydrological connection is confirmed, this would to our knowledge be the first indication of hydrologically connected subglacial lakes in Greenland.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"67 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139802217","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}
Louise Sandberg Sørensen, R. Bahbah, S. Simonsen, Natalia Havelund Andersen, Jade S. Bowling, N. Gourmelen, Alexander J. Horton, N. Karlsson, A. Leeson, Jennifer Maddalena, M. McMillan, A. Solgaard, B. Wessel
Abstract. Subglacial lakes form beneath ice sheets and ice caps if water is available and if bedrock and surface topography are able to retain the water. On a regional scale, the lakes modulate the timing and rate of freshwater flow through the subglacial system to the ocean by acting as reservoirs. More than 100 hydrologically active subglacial lakes that drain and recharge periodically have been documented under the Antarctic Ice Sheet, while only approximately 20 active lakes have been identified in Greenland. Active lakes may be identified by local changes in ice topography caused by the drainage or recharge of the lake beneath the ice. The small size of the Greenlandic subglacial lakes puts additional demands on mapping capabilities to resolve the evolving surface topography in sufficient detail to record their temporal behaviour. Here, we explore the potential for using CryoSat-2 swath-processed data, together with TanDEM-X digital elevation models, to improve the monitoring capabilities of active subglacial lakes in Greenland. We focus on four subglacial lakes previously described in the literature and combine the data with ArcticDEMs to obtain improved measurements of the evolution of these four lakes. We find that with careful tuning of the swath processor and filtering of the output data, the inclusion of these data, together with the TanDEM-X data, provides important information on lake activity, documenting, for example, that the ice surface collapse basin on Flade Isblink Ice Cap was 50 % (30 m) deeper than previously recorded. We also present evidence of a new, active subglacial lake in southwestern Greenland, which is located close to an already known lake. Both lakes probably drained within 1 month in the summer of 2012, which suggests either that they are hydrologically connected or that the drainages were independently triggered by extensive surface melt. If the hydrological connection is confirmed, this would to our knowledge be the first indication of hydrologically connected subglacial lakes in Greenland.
{"title":"Improved monitoring of subglacial lake activity in Greenland","authors":"Louise Sandberg Sørensen, R. Bahbah, S. Simonsen, Natalia Havelund Andersen, Jade S. Bowling, N. Gourmelen, Alexander J. Horton, N. Karlsson, A. Leeson, Jennifer Maddalena, M. McMillan, A. Solgaard, B. Wessel","doi":"10.5194/tc-18-505-2024","DOIUrl":"https://doi.org/10.5194/tc-18-505-2024","url":null,"abstract":"Abstract. Subglacial lakes form beneath ice sheets and ice caps if water is available and if bedrock and surface topography are able to retain the water. On a regional scale, the lakes modulate the timing and rate of freshwater flow through the subglacial system to the ocean by acting as reservoirs. More than 100 hydrologically active subglacial lakes that drain and recharge periodically have been documented under the Antarctic Ice Sheet, while only approximately 20 active lakes have been identified in Greenland. Active lakes may be identified by local changes in ice topography caused by the drainage or recharge of the lake beneath the ice. The small size of the Greenlandic subglacial lakes puts additional demands on mapping capabilities to resolve the evolving surface topography in sufficient detail to record their temporal behaviour. Here, we explore the potential for using CryoSat-2 swath-processed data, together with TanDEM-X digital elevation models, to improve the monitoring capabilities of active subglacial lakes in Greenland. We focus on four subglacial lakes previously described in the literature and combine the data with ArcticDEMs to obtain improved measurements of the evolution of these four lakes. We find that with careful tuning of the swath processor and filtering of the output data, the inclusion of these data, together with the TanDEM-X data, provides important information on lake activity, documenting, for example, that the ice surface collapse basin on Flade Isblink Ice Cap was 50 % (30 m) deeper than previously recorded. We also present evidence of a new, active subglacial lake in southwestern Greenland, which is located close to an already known lake. Both lakes probably drained within 1 month in the summer of 2012, which suggests either that they are hydrologically connected or that the drainages were independently triggered by extensive surface melt. If the hydrological connection is confirmed, this would to our knowledge be the first indication of hydrologically connected subglacial lakes in Greenland.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"40 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139861932","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}
J. Pumple, A. Monteath, Jordan Harvey, M. Roustaei, Alejandro Alvarez, Casey Buchanan, Duane Froese
Abstract. Permafrost cores provide physical samples that can be used to measure the characteristics of frozen ground. Measurements of core physical properties, however, are typically destructive and time intensive. In this study, multi-sensor core logging (MSCL) is used to provide a rapid (∼2–3 cm core depth per minute), high-resolution, non-destructive method to image and collect the physical properties of permafrost cores, allowing for the visualization of cryostructures and estimation of frozen bulk density, magnetic susceptibility, and volumetric ice content. Six permafrost cores with differing properties were analyzed using MSCL and compared with established destructive analyses to assess the potential of this instrument both in terms of accuracy and relative rate of data acquisition. A calibration procedure is presented for gamma ray attenuation from a 137Cs source that is specific to frozen-core materials. This accurately estimates frozen bulk density over the wide range of material densities found in permafrost. MSCL frozen bulk density data show agreement with destructive analyses based on discrete-sample measurements, with an RMSE of 0.067 g cm−3. Frozen bulk density data from the gamma attenuation, along with soil dry bulk density measurements for different sediment types, are used to estimate volumetric ice content. This approach does require an estimation of the soil dry bulk density and assumption of air content. However, the averaged results for this method show good agreement with an RMSE of 6.7 %, illustrating MSCL can provide non-destructive estimates of volumetric ice contents and a digital archive of permafrost cores for future applications.
{"title":"Non-destructive multi-sensor core logging allows for rapid imaging and estimation of frozen bulk density and volumetric ice content in permafrost cores","authors":"J. Pumple, A. Monteath, Jordan Harvey, M. Roustaei, Alejandro Alvarez, Casey Buchanan, Duane Froese","doi":"10.5194/tc-18-489-2024","DOIUrl":"https://doi.org/10.5194/tc-18-489-2024","url":null,"abstract":"Abstract. Permafrost cores provide physical samples that can be used to measure the characteristics of frozen ground. Measurements of core physical properties, however, are typically destructive and time intensive. In this study, multi-sensor core logging (MSCL) is used to provide a rapid (∼2–3 cm core depth per minute), high-resolution, non-destructive method to image and collect the physical properties of permafrost cores, allowing for the visualization of cryostructures and estimation of frozen bulk density, magnetic susceptibility, and volumetric ice content. Six permafrost cores with differing properties were analyzed using MSCL and compared with established destructive analyses to assess the potential of this instrument both in terms of accuracy and relative rate of data acquisition. A calibration procedure is presented for gamma ray attenuation from a 137Cs source that is specific to frozen-core materials. This accurately estimates frozen bulk density over the wide range of material densities found in permafrost. MSCL frozen bulk density data show agreement with destructive analyses based on discrete-sample measurements, with an RMSE of 0.067 g cm−3. Frozen bulk density data from the gamma attenuation, along with soil dry bulk density measurements for different sediment types, are used to estimate volumetric ice content. This approach does require an estimation of the soil dry bulk density and assumption of air content. However, the averaged results for this method show good agreement with an RMSE of 6.7 %, illustrating MSCL can provide non-destructive estimates of volumetric ice contents and a digital archive of permafrost cores for future applications.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"47 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139871542","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}
J. Pumple, A. Monteath, Jordan Harvey, M. Roustaei, Alejandro Alvarez, Casey Buchanan, Duane Froese
Abstract. Permafrost cores provide physical samples that can be used to measure the characteristics of frozen ground. Measurements of core physical properties, however, are typically destructive and time intensive. In this study, multi-sensor core logging (MSCL) is used to provide a rapid (∼2–3 cm core depth per minute), high-resolution, non-destructive method to image and collect the physical properties of permafrost cores, allowing for the visualization of cryostructures and estimation of frozen bulk density, magnetic susceptibility, and volumetric ice content. Six permafrost cores with differing properties were analyzed using MSCL and compared with established destructive analyses to assess the potential of this instrument both in terms of accuracy and relative rate of data acquisition. A calibration procedure is presented for gamma ray attenuation from a 137Cs source that is specific to frozen-core materials. This accurately estimates frozen bulk density over the wide range of material densities found in permafrost. MSCL frozen bulk density data show agreement with destructive analyses based on discrete-sample measurements, with an RMSE of 0.067 g cm−3. Frozen bulk density data from the gamma attenuation, along with soil dry bulk density measurements for different sediment types, are used to estimate volumetric ice content. This approach does require an estimation of the soil dry bulk density and assumption of air content. However, the averaged results for this method show good agreement with an RMSE of 6.7 %, illustrating MSCL can provide non-destructive estimates of volumetric ice contents and a digital archive of permafrost cores for future applications.
{"title":"Non-destructive multi-sensor core logging allows for rapid imaging and estimation of frozen bulk density and volumetric ice content in permafrost cores","authors":"J. Pumple, A. Monteath, Jordan Harvey, M. Roustaei, Alejandro Alvarez, Casey Buchanan, Duane Froese","doi":"10.5194/tc-18-489-2024","DOIUrl":"https://doi.org/10.5194/tc-18-489-2024","url":null,"abstract":"Abstract. Permafrost cores provide physical samples that can be used to measure the characteristics of frozen ground. Measurements of core physical properties, however, are typically destructive and time intensive. In this study, multi-sensor core logging (MSCL) is used to provide a rapid (∼2–3 cm core depth per minute), high-resolution, non-destructive method to image and collect the physical properties of permafrost cores, allowing for the visualization of cryostructures and estimation of frozen bulk density, magnetic susceptibility, and volumetric ice content. Six permafrost cores with differing properties were analyzed using MSCL and compared with established destructive analyses to assess the potential of this instrument both in terms of accuracy and relative rate of data acquisition. A calibration procedure is presented for gamma ray attenuation from a 137Cs source that is specific to frozen-core materials. This accurately estimates frozen bulk density over the wide range of material densities found in permafrost. MSCL frozen bulk density data show agreement with destructive analyses based on discrete-sample measurements, with an RMSE of 0.067 g cm−3. Frozen bulk density data from the gamma attenuation, along with soil dry bulk density measurements for different sediment types, are used to estimate volumetric ice content. This approach does require an estimation of the soil dry bulk density and assumption of air content. However, the averaged results for this method show good agreement with an RMSE of 6.7 %, illustrating MSCL can provide non-destructive estimates of volumetric ice contents and a digital archive of permafrost cores for future applications.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"29 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139811437","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}
Idunn Aamnes Mostue, Stefan Hofer, T. Storelvmo, X. Fettweis
Abstract. The Greenland Ice Sheet (GrIS) has been losing mass since the 1990s as a direct consequence of rising temperatures and has been projected to continue to lose mass at an accelerating pace throughout the 21st century, making it one of the largest contributors to future sea-level rise. The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) models produce a greater Arctic amplification signal and therefore also a notably larger mass loss from the GrIS when compared to the older CMIP5 projections, despite similar forcing levels from greenhouse gas emissions. However, it is also argued that the strength of regional factors, such as melt–albedo feedbacks and cloud-related feedbacks, will partly impact future melt and sea-level rise contribution, yet little is known about the role of these regional factors in producing differences in GrIS surface melt projections between CMIP6 and CMIP5. In this study, we use high-resolution (15 km) regional climate model simulations over the GrIS performed using the Modèle Atmosphérique Régional (MAR) to physically downscale six CMIP5 Representative Concentration Pathway (RCP) 8.5 and five CMIP6 Shared Socioeconomic Pathway (SSP) 5-8.5 extreme high-emission-scenario simulations. Here, we show a greater annual mass loss from the GrIS at the end of the 21st century but also for a given temperature increase over the GrIS, when comparing CMIP6 to CMIP5. We find a greater sensitivity of Greenland surface mass loss in CMIP6 centred around summer and autumn, yet the difference in mass loss is the largest during autumn with a reduction of 27.7 ± 9.5 Gt per season for a regional warming of +6.7 ∘C and 24.6 Gt per season more mass loss than in CMIP5 RCP8.5 simulations for the same warming. Assessment of the surface energy budget and cloud-related feedbacks suggests a reduction in high clouds during summer and autumn – despite enhanced cloud optical depth during autumn – to be the main driver of the additional energy reaching the surface, subsequently leading to enhanced surface melt and mass loss in CMIP6 compared to CMIP5. Our analysis highlights that Greenland is losing more mass in CMIP6 due to two factors: (1) a (known) greater sensitivity to greenhouse gas emissions and therefore warmer temperatures and (2) previously unnotified cloud-related surface energy budget changes that enhance the GrIS sensitivity to warming.
{"title":"Cloud- and ice-albedo feedbacks drive greater Greenland Ice Sheet sensitivity to warming in CMIP6 than in CMIP5","authors":"Idunn Aamnes Mostue, Stefan Hofer, T. Storelvmo, X. Fettweis","doi":"10.5194/tc-18-475-2024","DOIUrl":"https://doi.org/10.5194/tc-18-475-2024","url":null,"abstract":"Abstract. The Greenland Ice Sheet (GrIS) has been losing mass since the 1990s as a direct consequence of rising temperatures and has been projected to continue to lose mass at an accelerating pace throughout the 21st century, making it one of the largest contributors to future sea-level rise. The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) models produce a greater Arctic amplification signal and therefore also a notably larger mass loss from the GrIS when compared to the older CMIP5 projections, despite similar forcing levels from greenhouse gas emissions. However, it is also argued that the strength of regional factors, such as melt–albedo feedbacks and cloud-related feedbacks, will partly impact future melt and sea-level rise contribution, yet little is known about the role of these regional factors in producing differences in GrIS surface melt projections between CMIP6 and CMIP5. In this study, we use high-resolution (15 km) regional climate model simulations over the GrIS performed using the Modèle Atmosphérique Régional (MAR) to physically downscale six CMIP5 Representative Concentration Pathway (RCP) 8.5 and five CMIP6 Shared Socioeconomic Pathway (SSP) 5-8.5 extreme high-emission-scenario simulations. Here, we show a greater annual mass loss from the GrIS at the end of the 21st century but also for a given temperature increase over the GrIS, when comparing CMIP6 to CMIP5. We find a greater sensitivity of Greenland surface mass loss in CMIP6 centred around summer and autumn, yet the difference in mass loss is the largest during autumn with a reduction of 27.7 ± 9.5 Gt per season for a regional warming of +6.7 ∘C and 24.6 Gt per season more mass loss than in CMIP5 RCP8.5 simulations for the same warming. Assessment of the surface energy budget and cloud-related feedbacks suggests a reduction in high clouds during summer and autumn – despite enhanced cloud optical depth during autumn – to be the main driver of the additional energy reaching the surface, subsequently leading to enhanced surface melt and mass loss in CMIP6 compared to CMIP5. Our analysis highlights that Greenland is losing more mass in CMIP6 due to two factors: (1) a (known) greater sensitivity to greenhouse gas emissions and therefore warmer temperatures and (2) previously unnotified cloud-related surface energy budget changes that enhance the GrIS sensitivity to warming.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"50 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139873437","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}
Idunn Aamnes Mostue, Stefan Hofer, T. Storelvmo, X. Fettweis
Abstract. The Greenland Ice Sheet (GrIS) has been losing mass since the 1990s as a direct consequence of rising temperatures and has been projected to continue to lose mass at an accelerating pace throughout the 21st century, making it one of the largest contributors to future sea-level rise. The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) models produce a greater Arctic amplification signal and therefore also a notably larger mass loss from the GrIS when compared to the older CMIP5 projections, despite similar forcing levels from greenhouse gas emissions. However, it is also argued that the strength of regional factors, such as melt–albedo feedbacks and cloud-related feedbacks, will partly impact future melt and sea-level rise contribution, yet little is known about the role of these regional factors in producing differences in GrIS surface melt projections between CMIP6 and CMIP5. In this study, we use high-resolution (15 km) regional climate model simulations over the GrIS performed using the Modèle Atmosphérique Régional (MAR) to physically downscale six CMIP5 Representative Concentration Pathway (RCP) 8.5 and five CMIP6 Shared Socioeconomic Pathway (SSP) 5-8.5 extreme high-emission-scenario simulations. Here, we show a greater annual mass loss from the GrIS at the end of the 21st century but also for a given temperature increase over the GrIS, when comparing CMIP6 to CMIP5. We find a greater sensitivity of Greenland surface mass loss in CMIP6 centred around summer and autumn, yet the difference in mass loss is the largest during autumn with a reduction of 27.7 ± 9.5 Gt per season for a regional warming of +6.7 ∘C and 24.6 Gt per season more mass loss than in CMIP5 RCP8.5 simulations for the same warming. Assessment of the surface energy budget and cloud-related feedbacks suggests a reduction in high clouds during summer and autumn – despite enhanced cloud optical depth during autumn – to be the main driver of the additional energy reaching the surface, subsequently leading to enhanced surface melt and mass loss in CMIP6 compared to CMIP5. Our analysis highlights that Greenland is losing more mass in CMIP6 due to two factors: (1) a (known) greater sensitivity to greenhouse gas emissions and therefore warmer temperatures and (2) previously unnotified cloud-related surface energy budget changes that enhance the GrIS sensitivity to warming.
{"title":"Cloud- and ice-albedo feedbacks drive greater Greenland Ice Sheet sensitivity to warming in CMIP6 than in CMIP5","authors":"Idunn Aamnes Mostue, Stefan Hofer, T. Storelvmo, X. Fettweis","doi":"10.5194/tc-18-475-2024","DOIUrl":"https://doi.org/10.5194/tc-18-475-2024","url":null,"abstract":"Abstract. The Greenland Ice Sheet (GrIS) has been losing mass since the 1990s as a direct consequence of rising temperatures and has been projected to continue to lose mass at an accelerating pace throughout the 21st century, making it one of the largest contributors to future sea-level rise. The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) models produce a greater Arctic amplification signal and therefore also a notably larger mass loss from the GrIS when compared to the older CMIP5 projections, despite similar forcing levels from greenhouse gas emissions. However, it is also argued that the strength of regional factors, such as melt–albedo feedbacks and cloud-related feedbacks, will partly impact future melt and sea-level rise contribution, yet little is known about the role of these regional factors in producing differences in GrIS surface melt projections between CMIP6 and CMIP5. In this study, we use high-resolution (15 km) regional climate model simulations over the GrIS performed using the Modèle Atmosphérique Régional (MAR) to physically downscale six CMIP5 Representative Concentration Pathway (RCP) 8.5 and five CMIP6 Shared Socioeconomic Pathway (SSP) 5-8.5 extreme high-emission-scenario simulations. Here, we show a greater annual mass loss from the GrIS at the end of the 21st century but also for a given temperature increase over the GrIS, when comparing CMIP6 to CMIP5. We find a greater sensitivity of Greenland surface mass loss in CMIP6 centred around summer and autumn, yet the difference in mass loss is the largest during autumn with a reduction of 27.7 ± 9.5 Gt per season for a regional warming of +6.7 ∘C and 24.6 Gt per season more mass loss than in CMIP5 RCP8.5 simulations for the same warming. Assessment of the surface energy budget and cloud-related feedbacks suggests a reduction in high clouds during summer and autumn – despite enhanced cloud optical depth during autumn – to be the main driver of the additional energy reaching the surface, subsequently leading to enhanced surface melt and mass loss in CMIP6 compared to CMIP5. Our analysis highlights that Greenland is losing more mass in CMIP6 due to two factors: (1) a (known) greater sensitivity to greenhouse gas emissions and therefore warmer temperatures and (2) previously unnotified cloud-related surface energy budget changes that enhance the GrIS sensitivity to warming.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"25 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139813477","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}
D. K. Singh, S. Tanniru, K. Singh, H. S. Negi, R. Ramsankaran
Abstract. Spatiotemporal snow depth (SD) mapping in the Indian Western Himalayan (WH) region is essential in many applications pertaining to hydrology, natural disaster management, climate, etc. In situ techniques for SD measurement are not sufficient to represent the high spatiotemporal variability in SD in the WH region. Currently, low-frequency passive microwave (PMW) remote-sensing-based algorithms are extensively used to monitor SD at regional and global scales. However, fewer PMW SD estimation studies have been carried out for the WH region to date, which are mainly confined to small subregions of the WH region. In addition, the majority of the available PMW SD models for WH locations are developed using limited data and fewer parameters and therefore cannot be implemented for the entire region. Further, these models have not taken the auxiliary parameters such as location, topography, and snow cover duration (SCD) into consideration and have poor accuracy (particularly in deep snow) and coarse spatial resolution. Considering the high spatiotemporal variability in snow depth characteristics across the WH region, region-wise multifactor models are developed for the first time to estimate SD at a high spatial resolution of 500 m × 500 m for three different WH zones, i.e., Lower Himalayan Zone (LHZ), Middle Himalayan Zone (MHZ), and Upper Himalayan Zone (UHZ). Multifrequency brightness temperature (TB) observations from Advanced Microwave Scanning Radiometer 2 (AMSR2), SCD data, terrain parameters (i.e., elevation, slope, and ruggedness), and geolocation for the winter period (October to March) during 2012–2013 to 2016–2017 are used for developing the SD models for dry snow conditions. Different regression approaches (i.e., linear, logarithmic, reciprocal, and power) are used to develop snow depth models, which are evaluated further to find if any of these models can address the heterogeneous association between SD observations and PMW TB. From the results, it is observed from the analysis that the power regression SD model has improved accuracy in all WH zones with the low root mean square error (RMSE) in the MHZ (i.e., 27.21 cm) compared to the LHZ (32.87 cm) and the UHZ (42.81 cm). The spatial distribution of model-derived SD is highly affected by SCD, terrain parameters, and geolocation parameters and has better SD estimates compared to regional and global products in all zones. Overall results indicate that the proposed multifactor SD models have achieved higher accuracy in deep snowpack (i.e., SD >25 cm) of the WH region compared to previously developed SD models.
{"title":"Passive microwave remote-sensing-based high-resolution snow depth mapping for Western Himalayan zones using multifactor modeling approach","authors":"D. K. Singh, S. Tanniru, K. Singh, H. S. Negi, R. Ramsankaran","doi":"10.5194/tc-18-451-2024","DOIUrl":"https://doi.org/10.5194/tc-18-451-2024","url":null,"abstract":"Abstract. Spatiotemporal snow depth (SD) mapping in the Indian Western Himalayan (WH) region is essential in many applications pertaining to hydrology, natural disaster management, climate, etc. In situ techniques for SD measurement are not sufficient to represent the high spatiotemporal variability in SD in the WH region. Currently, low-frequency passive microwave (PMW) remote-sensing-based algorithms are extensively used to monitor SD at regional and global scales. However, fewer PMW SD estimation studies have been carried out for the WH region to date, which are mainly confined to small subregions of the WH region. In addition, the majority of the available PMW SD models for WH locations are developed using limited data and fewer parameters and therefore cannot be implemented for the entire region. Further, these models have not taken the auxiliary parameters such as location, topography, and snow cover duration (SCD) into consideration and have poor accuracy (particularly in deep snow) and coarse spatial resolution. Considering the high spatiotemporal variability in snow depth characteristics across the WH region, region-wise multifactor models are developed for the first time to estimate SD at a high spatial resolution of 500 m × 500 m for three different WH zones, i.e., Lower Himalayan Zone (LHZ), Middle Himalayan Zone (MHZ), and Upper Himalayan Zone (UHZ). Multifrequency brightness temperature (TB) observations from Advanced Microwave Scanning Radiometer 2 (AMSR2), SCD data, terrain parameters (i.e., elevation, slope, and ruggedness), and geolocation for the winter period (October to March) during 2012–2013 to 2016–2017 are used for developing the SD models for dry snow conditions. Different regression approaches (i.e., linear, logarithmic, reciprocal, and power) are used to develop snow depth models, which are evaluated further to find if any of these models can address the heterogeneous association between SD observations and PMW TB. From the results, it is observed from the analysis that the power regression SD model has improved accuracy in all WH zones with the low root mean square error (RMSE) in the MHZ (i.e., 27.21 cm) compared to the LHZ (32.87 cm) and the UHZ (42.81 cm). The spatial distribution of model-derived SD is highly affected by SCD, terrain parameters, and geolocation parameters and has better SD estimates compared to regional and global products in all zones. Overall results indicate that the proposed multifactor SD models have achieved higher accuracy in deep snowpack (i.e., SD >25 cm) of the WH region compared to previously developed SD models.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"87 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140470793","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}
Ryo Inoue, Shuji Fujita, K. Kawamura, Ikumi Oyabu, F. Nakazawa, Hideaki Motoyama, T. Aoki
Abstract. To better understand the near-surface evolution of polar firn in low-accumulation areas (<30 mm w.e. yr−1), we investigated the physical properties – density, microstructural anisotropy of ice matrix and pore space, and specific surface area (SSA) – of six firn cores collected within 60 km of Dome Fuji, East Antarctica. The physical properties were measured at intervals of ≤0.02 m over the top 10 m of the cores. The main findings are (i) a lack of significant density increase in the top ∼4 m, (ii) lower mean density near the dome summit (∼330 kg m−3) than in the surrounding slope area (∼355 kg m−3) in the top 1 m, (iii) developments of a vertically elongated microstructure and its contrast between layers within the top ∼3 m, (iv) more pronounced vertical elongation at sites and periods with lower accumulation rates than those with higher accumulation rates, (v) a rapid decrease in SSA in the top ∼3 m, and (vi) lower SSA at lower-accumulation sites, but this latter trend is less pronounced than that of microstructural anisotropy. These observations can be explained by a combination of the initial physical properties on the surface set by wind conditions and the metamorphism driven by water vapor transport through the firn column under a strong vertical temperature gradient (temperature gradient metamorphism, TGM). The magnitude of TGM depends on the duration of firn layers under the temperature gradient, determined by the accumulation rate; longer exposure causes a more vertically elongated microstructure and lower SSA. Overall, we highlight the significant spatial variability in the near-surface physical properties over the scale of ∼100 km around Dome Fuji. These findings will help us better understand the densification over the whole firn column and the gas-trapping process in deep firn and possible difference in them between existing deep ice cores and the upcoming “Oldest-Ice” cores collected tens of kilometers apart.
摘要为了更好地了解低积累区（<30 mm w.e. yr-1）极地杉岩的近地表演化，我们研究了在南极洲东部富士圆顶60千米范围内采集的6个杉岩岩心的物理性质--密度、冰基质和孔隙空间的微结构各向异性以及比表面积（SSA）。物理特性是在岩芯顶部 10 米处以 ≤0.02 米的间隔测量的。主要发现有：(i) 顶部∼4 米的密度没有明显增加；(ii) 穹顶顶附近顶部 1 米的平均密度（∼330 kg m-3）低于周围斜坡区域（∼355 kg m-3）；(iii) 垂直拉长的微观结构及其顶部∼3 米内各层之间的对比发展、(iv)在堆积率较低的地点和时期，垂直伸长比在堆积率较高的地点和时期更为明显；(v)在顶部 ∼3 m，SSA 快速下降；(vi)在堆积率较低的地点，SSA 较低，但后一种趋势不如微结构各向异性的趋势明显。这些观测结果可以解释为：由风力条件设定的地表初始物理性质，以及在强烈的垂直温度梯度（温度梯度变质作用，TGM）作用下，通过枞树柱的水汽输送驱动的变质作用。温度梯度变质的程度取决于枞树层在温度梯度下的持续时间，由累积率决定；暴露时间越长，微观结构的垂直伸长越大，SSA越低。总之，我们强调了富士圆顶周围∼100 km范围内近地表物理特性的显著空间变化。这些发现将有助于我们更好地理解整个枞树柱的致密化和深层枞树的气体捕集过程，以及现有深冰芯和即将采集的相距数十公里的 "最老冰 "芯之间可能存在的差异。
{"title":"Spatial distribution of vertical density and microstructure profiles in near-surface firn around Dome Fuji, Antarctica","authors":"Ryo Inoue, Shuji Fujita, K. Kawamura, Ikumi Oyabu, F. Nakazawa, Hideaki Motoyama, T. Aoki","doi":"10.5194/tc-18-425-2024","DOIUrl":"https://doi.org/10.5194/tc-18-425-2024","url":null,"abstract":"Abstract. To better understand the near-surface evolution of polar firn in low-accumulation areas (<30 mm w.e. yr−1), we investigated the physical properties – density, microstructural anisotropy of ice matrix and pore space, and specific surface area (SSA) – of six firn cores collected within 60 km of Dome Fuji, East Antarctica. The physical properties were measured at intervals of ≤0.02 m over the top 10 m of the cores. The main findings are (i) a lack of significant density increase in the top ∼4 m, (ii) lower mean density near the dome summit (∼330 kg m−3) than in the surrounding slope area (∼355 kg m−3) in the top 1 m, (iii) developments of a vertically elongated microstructure and its contrast between layers within the top ∼3 m, (iv) more pronounced vertical elongation at sites and periods with lower accumulation rates than those with higher accumulation rates, (v) a rapid decrease in SSA in the top ∼3 m, and (vi) lower SSA at lower-accumulation sites, but this latter trend is less pronounced than that of microstructural anisotropy. These observations can be explained by a combination of the initial physical properties on the surface set by wind conditions and the metamorphism driven by water vapor transport through the firn column under a strong vertical temperature gradient (temperature gradient metamorphism, TGM). The magnitude of TGM depends on the duration of firn layers under the temperature gradient, determined by the accumulation rate; longer exposure causes a more vertically elongated microstructure and lower SSA. Overall, we highlight the significant spatial variability in the near-surface physical properties over the scale of ∼100 km around Dome Fuji. These findings will help us better understand the densification over the whole firn column and the gas-trapping process in deep firn and possible difference in them between existing deep ice cores and the upcoming “Oldest-Ice” cores collected tens of kilometers apart.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"93 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140484733","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}
V. Tyystjärvi, P. Niittynen, J. Kemppinen, M. Luoto, T. Rissanen, J. Aalto
Abstract. Winter near-surface air temperatures have important implications for ecosystem functioning such as vegetation dynamics and carbon cycling. In cold environments, the persistence of seasonal snow cover can exert a strong control on the near-surface temperatures. However, the lack of in situ measurements of both snow cover duration and surface temperatures over high latitudes has made it difficult to estimate the spatio-temporal variability in this relationship. Here, we quantified the fine-scale variability in winter near-surface air temperatures (+2 cm) and snow cover duration (calculated from temperature time series) using a total of 441 microclimate loggers in seven study areas across boreal and tundra landscapes in Finland during 2019–2021. We further examined the drivers behind this variation using a structural equation model and the extent to which near-surface air temperatures are buffered from free-air temperatures during winter. Our results show that while average winter near-surface temperatures stay close to 0 ∘C across the study domain, there are large differences in their fine-scale variability among the study areas. Areas with large topographical variation, as well as areas with shallow snowpacks, showed the greatest variation in near-surface temperatures and in snow cover duration. In the tundra, for example, differences in minimum near-surface temperatures between study sites were close to 30 ∘C and topography was shown to be an important driver of this variability. In contrast, flat topography and long snow cover duration led to little spatial variation, as well as long periods of decoupling between near-surface and air temperatures. Quantifying and understanding the landscape-wide variation in winter microclimates improves our ability to predict the local effects of climate change in the rapidly warming boreal and tundra regions.
{"title":"Variability and drivers of winter near-surface temperatures over boreal and tundra landscapes","authors":"V. Tyystjärvi, P. Niittynen, J. Kemppinen, M. Luoto, T. Rissanen, J. Aalto","doi":"10.5194/tc-18-403-2024","DOIUrl":"https://doi.org/10.5194/tc-18-403-2024","url":null,"abstract":"Abstract. Winter near-surface air temperatures have important implications for ecosystem functioning such as vegetation dynamics and carbon cycling. In cold environments, the persistence of seasonal snow cover can exert a strong control on the near-surface temperatures. However, the lack of in situ measurements of both snow cover duration and surface temperatures over high latitudes has made it difficult to estimate the spatio-temporal variability in this relationship. Here, we quantified the fine-scale variability in winter near-surface air temperatures (+2 cm) and snow cover duration (calculated from temperature time series) using a total of 441 microclimate loggers in seven study areas across boreal and tundra landscapes in Finland during 2019–2021. We further examined the drivers behind this variation using a structural equation model and the extent to which near-surface air temperatures are buffered from free-air temperatures during winter. Our results show that while average winter near-surface temperatures stay close to 0 ∘C across the study domain, there are large differences in their fine-scale variability among the study areas. Areas with large topographical variation, as well as areas with shallow snowpacks, showed the greatest variation in near-surface temperatures and in snow cover duration. In the tundra, for example, differences in minimum near-surface temperatures between study sites were close to 30 ∘C and topography was shown to be an important driver of this variability. In contrast, flat topography and long snow cover duration led to little spatial variation, as well as long periods of decoupling between near-surface and air temperatures. Quantifying and understanding the landscape-wide variation in winter microclimates improves our ability to predict the local effects of climate change in the rapidly warming boreal and tundra regions.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"20 2-3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140490326","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}
Ling-li Li, F. Cannon, M. Mazloff, A. Subramanian, Anna M. Wilson, F. Ralph
Abstract. Arctic sea ice has been declining rapidly in recent decades. We investigate how the poleward transport of moisture and heat from lower latitudes through atmospheric rivers (ARs) influences Arctic sea ice variations. We use hourly ERA5 (fifth-generation European Reanalysis) data for 1981–2020 at 0.25∘ × 0.25∘ resolution to examine the meteorological conditions and sea ice changes associated with ARs in the Arctic. In the years 2012 and 2020, which had an extremely low summer Arctic sea ice extent, we show that the individual AR events associated with large cyclones initiate a rapid sea ice decrease through turbulent heat fluxes and winds. We carry out further statistical analysis of the meteorological conditions and sea ice variations for 1981–2020 over the entire Arctic Ocean. We find that on weather timescales the atmospheric moisture content anticorrelates significantly with the sea ice concentration tendency almost everywhere in the Arctic Ocean, while the dynamic sea ice motion driven by northward winds further reduces the sea ice concentration.
{"title":"Impact of atmospheric rivers on Arctic sea ice variations","authors":"Ling-li Li, F. Cannon, M. Mazloff, A. Subramanian, Anna M. Wilson, F. Ralph","doi":"10.5194/tc-18-121-2024","DOIUrl":"https://doi.org/10.5194/tc-18-121-2024","url":null,"abstract":"Abstract. Arctic sea ice has been declining rapidly in recent decades. We investigate how the poleward transport of moisture and heat from lower latitudes through atmospheric rivers (ARs) influences Arctic sea ice variations. We use hourly ERA5 (fifth-generation European Reanalysis) data for 1981–2020 at 0.25∘ × 0.25∘ resolution to examine the meteorological conditions and sea ice changes associated with ARs in the Arctic. In the years 2012 and 2020, which had an extremely low summer Arctic sea ice extent, we show that the individual AR events associated with large cyclones initiate a rapid sea ice decrease through turbulent heat fluxes and winds. We carry out further statistical analysis of the meteorological conditions and sea ice variations for 1981–2020 over the entire Arctic Ocean. We find that on weather timescales the atmospheric moisture content anticorrelates significantly with the sea ice concentration tendency almost everywhere in the Arctic Ocean, while the dynamic sea ice motion driven by northward winds further reduces the sea ice concentration.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"52 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139385016","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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