Charlotte Durand, T. Finn, A. Farchi, M. Bocquet, Einar Örn Ólason
Abstract. A novel generation of sea-ice models with elasto-brittle rheologies, such as neXtSIM, can represent sea-ice processes with an unprecedented accuracy at the mesoscale for resolutions of around 10 km. As these models are computationally expensive, we introduce supervised deep learning techniques for surrogate modeling of the sea-ice thickness from neXtSIM simulations. We adapt a convolutional U-Net architecture to an Arctic-wide setup by taking the land–sea mask with partial convolutions into account. Trained to emulate the sea-ice thickness at a lead time of 12 h, the neural network can be iteratively applied to predictions for up to 1 year. The improvements of the surrogate model over a persistence forecast persist from 12 h to roughly 1 year, with improvements of up to 50 % in the forecast error. Moreover, the predictability gain for the sea-ice thickness measured against the daily climatology extends to over 6 months. By using atmospheric forcings as additional input, the surrogate model can represent advective and thermodynamical processes which influence the sea-ice thickness and the growth and melting therein. While iterating, the surrogate model experiences diffusive processes which result in a loss of fine-scale structures. However, this smoothing increases the coherence of large-scale features and thereby the stability of the model. Therefore, based on these results, we see huge potential for surrogate modeling of state-of-the-art sea-ice models with neural networks.
{"title":"Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic","authors":"Charlotte Durand, T. Finn, A. Farchi, M. Bocquet, Einar Örn Ólason","doi":"10.5194/tc-18-1791-2024","DOIUrl":"https://doi.org/10.5194/tc-18-1791-2024","url":null,"abstract":"Abstract. A novel generation of sea-ice models with elasto-brittle rheologies, such as neXtSIM, can represent sea-ice processes with an unprecedented accuracy at the mesoscale for resolutions of around 10 km. As these models are computationally expensive, we introduce supervised deep learning techniques for surrogate modeling of the sea-ice thickness from neXtSIM simulations. We adapt a convolutional U-Net architecture to an Arctic-wide setup by taking the land–sea mask with partial convolutions into account. Trained to emulate the sea-ice thickness at a lead time of 12 h, the neural network can be iteratively applied to predictions for up to 1 year. The improvements of the surrogate model over a persistence forecast persist from 12 h to roughly 1 year, with improvements of up to 50 % in the forecast error. Moreover, the predictability gain for the sea-ice thickness measured against the daily climatology extends to over 6 months. By using atmospheric forcings as additional input, the surrogate model can represent advective and thermodynamical processes which influence the sea-ice thickness and the growth and melting therein. While iterating, the surrogate model experiences diffusive processes which result in a loss of fine-scale structures. However, this smoothing increases the coherence of large-scale features and thereby the stability of the model. Therefore, based on these results, we see huge potential for surrogate modeling of state-of-the-art sea-ice models with neural networks.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140690012","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}
Samuel Valman, Matthias Siewert, Doreen S. Boyd, Martha J Ledger, David Gee, Betsabé de la Barreda-Bautista, A. Sowter, Sofie Sjögersten
Abstract. Climate warming is degrading palsa peatlands across the circumpolar permafrost region. Permafrost degradation may lead to ecosystem collapse and potentially strong climate feedbacks, as this ecosystem is an important carbon store and can transition to being a strong greenhouse gas emitter. Landscape-level measurement of permafrost degradation is needed to monitor this impact of warming. Surface subsidence is a useful metric of change in palsa degradation and can be monitored using interferometric synthetic-aperture radar (InSAR) satellite technology. We combined InSAR data, processed using the ASPIS algorithm to monitor ground motion between 2017 and 2021, with airborne optical and lidar data to investigate the rate of subsidence across palsa peatlands in northern Sweden. We show that 55 % of Sweden's eight largest palsa peatlands are currently subsiding, which can be attributed to the underlying permafrost landforms and their degradation. The most rapid degradation has occurred in the largest palsa complexes in the most northern part of the region of study, also corresponding to the areas with the highest percentage of palsa cover within the overall mapped wetland area. Further, higher degradation rates have been found in areas where winter precipitation has increased substantially. The roughness index calculated from a lidar-derived digital elevation model (DEM), used as a proxy for degradation, increases alongside subsidence rates and may be used as a complementary proxy for palsa degradation. We show that combining datasets captured using remote sensing enables regional-scale estimation of ongoing permafrost degradation, an important step towards estimating the future impact of climate change on permafrost-dependent ecosystems.
{"title":"InSAR-measured permafrost degradation of palsa peatlands in northern Sweden","authors":"Samuel Valman, Matthias Siewert, Doreen S. Boyd, Martha J Ledger, David Gee, Betsabé de la Barreda-Bautista, A. Sowter, Sofie Sjögersten","doi":"10.5194/tc-18-1773-2024","DOIUrl":"https://doi.org/10.5194/tc-18-1773-2024","url":null,"abstract":"Abstract. Climate warming is degrading palsa peatlands across the circumpolar permafrost region. Permafrost degradation may lead to ecosystem collapse and potentially strong climate feedbacks, as this ecosystem is an important carbon store and can transition to being a strong greenhouse gas emitter. Landscape-level measurement of permafrost degradation is needed to monitor this impact of warming. Surface subsidence is a useful metric of change in palsa degradation and can be monitored using interferometric synthetic-aperture radar (InSAR) satellite technology. We combined InSAR data, processed using the ASPIS algorithm to monitor ground motion between 2017 and 2021, with airborne optical and lidar data to investigate the rate of subsidence across palsa peatlands in northern Sweden. We show that 55 % of Sweden's eight largest palsa peatlands are currently subsiding, which can be attributed to the underlying permafrost landforms and their degradation. The most rapid degradation has occurred in the largest palsa complexes in the most northern part of the region of study, also corresponding to the areas with the highest percentage of palsa cover within the overall mapped wetland area. Further, higher degradation rates have been found in areas where winter precipitation has increased substantially. The roughness index calculated from a lidar-derived digital elevation model (DEM), used as a proxy for degradation, increases alongside subsidence rates and may be used as a complementary proxy for palsa degradation. We show that combining datasets captured using remote sensing enables regional-scale estimation of ongoing permafrost degradation, an important step towards estimating the future impact of climate change on permafrost-dependent ecosystems.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"56 s192","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140693903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Marcer, P. Duvillard, Soňa Tomaškovičová, S. Nielsen, André Revil, T. Ingeman‐Nielsen
Abstract. Degrading rock wall permafrost was found responsible for the increase in rockfall and landslide activity in several cold mountain regions across the globe. In Greenland, rock wall permafrost has so far received little attention from the scientific community, despite mountains being a predominant feature on the ice-free coastline and landslide activity being significant. In this study, we aim to make a first step towards a better understanding of rock wall permafrost in Greenland by modelling rock wall temperatures in the mountain area around the town of Sisimiut, which is 68° N on the west coast of Greenland. We first acquire rock surface temperature (RST) data for the period September 2020–September 2022 to model rock surface temperatures from weather forcing. The model is then applied to weather data from 1870 to 2022, generating rock surface temperatures to force transient heat transfer simulations over the same period. By extrapolating this method at the landscape scale, we obtain permafrost distribution maps and ad hoc simulations for complex topographies. Our model results are compared to temperature data from two lowland boreholes (100 m depth) and geophysical data describing frozen and unfrozen conditions across a mid-elevation mountain ridge. Finally, we use regional carbon pathway scenarios 2.6 and 8.5 to evaluate future evolution of rock wall temperatures until the end of the 21st century. Our data and simulation describe discontinuous permafrost distribution in rock walls up to roughly 400 m a.s.l. Future scenarios suggest a decline of deep frozen bodies up to 800 m a.s.l., i.e. the highest summits in the area. In summary, this study depicts a picture of warm permafrost in this area, highlighting its sensitivity to ongoing climate change.
{"title":"Modelling present and future rock wall permafrost distribution in the Sisimiut mountain area, West Greenland","authors":"M. Marcer, P. Duvillard, Soňa Tomaškovičová, S. Nielsen, André Revil, T. Ingeman‐Nielsen","doi":"10.5194/tc-18-1753-2024","DOIUrl":"https://doi.org/10.5194/tc-18-1753-2024","url":null,"abstract":"Abstract. Degrading rock wall permafrost was found responsible for the increase in rockfall and landslide activity in several cold mountain regions across the globe. In Greenland, rock wall permafrost has so far received little attention from the scientific community, despite mountains being a predominant feature on the ice-free coastline and landslide activity being significant. In this study, we aim to make a first step towards a better understanding of rock wall permafrost in Greenland by modelling rock wall temperatures in the mountain area around the town of Sisimiut, which is 68° N on the west coast of Greenland. We first acquire rock surface temperature (RST) data for the period September 2020–September 2022 to model rock surface temperatures from weather forcing. The model is then applied to weather data from 1870 to 2022, generating rock surface temperatures to force transient heat transfer simulations over the same period. By extrapolating this method at the landscape scale, we obtain permafrost distribution maps and ad hoc simulations for complex topographies. Our model results are compared to temperature data from two lowland boreholes (100 m depth) and geophysical data describing frozen and unfrozen conditions across a mid-elevation mountain ridge. Finally, we use regional carbon pathway scenarios 2.6 and 8.5 to evaluate future evolution of rock wall temperatures until the end of the 21st century. Our data and simulation describe discontinuous permafrost distribution in rock walls up to roughly 400 m a.s.l. Future scenarios suggest a decline of deep frozen bodies up to 800 m a.s.l., i.e. the highest summits in the area. In summary, this study depicts a picture of warm permafrost in this area, highlighting its sensitivity to ongoing climate change.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"38 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700517","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. Landscapes buried beneath the Antarctic Ice Sheet preserve information about the geologic and geomorphic evolution of the continent both before and during the wide-scale glaciation that began roughly 34×106 years ago. Since the inception of this ice sheet, some areas have remained cold-based and non-erosive, preserving ancient landscapes remarkably intact. The Gamburtsev Subglacial Mountains in central East Antarctica are one such landscape, maintaining evidence of tectonic, fluvial and glacial controls on their distinctly alpine morphology. The central Gamburtsev Mountains have previously been surveyed using airborne ice-penetrating radar; however, many questions remain as to their evolution and their influence on the East Antarctic Ice Sheet, including where in the region to drill for a 1.5×106 year-long “oldest-ice” core. Here, we derive new maps of the planform geometry of the Gamburtsev Subglacial Mountains from satellite remote sensing datasets of the ice sheet surface, based on the relationship between bed roughness and ice surface morphology. Automated and manual approaches to mapping were tested and validated against existing radar data and elevation models. Manual mapping was more effective than automated approaches at reproducing bed features observed in radar data, but a hybrid approach is suggested for future work. The maps produced here show the detail of mountain ridges and valleys on wavelengths significantly smaller than the spacing of existing radar flightlines, and mapping has extended well beyond the confines of existing radar surveys. Morphometric analysis of the mapped landscape reveals that it constitutes a preserved (>34 Ma) dendritic valley network, with some evidence for modification by topographically confined glaciation prior to ice sheet inception. The planform geometry of the landscape is a significant control on locations of basal melting, subglacial hydrological flows and the stability of the ice sheet over time, so the maps presented here may help to guide decisions about where to search for oldest ice.
{"title":"Alpine topography of the Gamburtsev Subglacial Mountains, Antarctica, mapped from ice sheet surface morphology","authors":"Edmund J. Lea, S. Jamieson, M. J. Bentley","doi":"10.5194/tc-18-1733-2024","DOIUrl":"https://doi.org/10.5194/tc-18-1733-2024","url":null,"abstract":"Abstract. Landscapes buried beneath the Antarctic Ice Sheet preserve information about the geologic and geomorphic evolution of the continent both before and during the wide-scale glaciation that began roughly 34×106 years ago. Since the inception of this ice sheet, some areas have remained cold-based and non-erosive, preserving ancient landscapes remarkably intact. The Gamburtsev Subglacial Mountains in central East Antarctica are one such landscape, maintaining evidence of tectonic, fluvial and glacial controls on their distinctly alpine morphology. The central Gamburtsev Mountains have previously been surveyed using airborne ice-penetrating radar; however, many questions remain as to their evolution and their influence on the East Antarctic Ice Sheet, including where in the region to drill for a 1.5×106 year-long “oldest-ice” core. Here, we derive new maps of the planform geometry of the Gamburtsev Subglacial Mountains from satellite remote sensing datasets of the ice sheet surface, based on the relationship between bed roughness and ice surface morphology. Automated and manual approaches to mapping were tested and validated against existing radar data and elevation models. Manual mapping was more effective than automated approaches at reproducing bed features observed in radar data, but a hybrid approach is suggested for future work. The maps produced here show the detail of mountain ridges and valleys on wavelengths significantly smaller than the spacing of existing radar flightlines, and mapping has extended well beyond the confines of existing radar surveys. Morphometric analysis of the mapped landscape reveals that it constitutes a preserved (>34 Ma) dendritic valley network, with some evidence for modification by topographically confined glaciation prior to ice sheet inception. The planform geometry of the landscape is a significant control on locations of basal melting, subglacial hydrological flows and the stability of the ice sheet over time, so the maps presented here may help to guide decisions about where to search for oldest ice.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"22 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140711513","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}
Naomi E. Ochwat, Ted A. Scambos, Alison F. Banwell, Robert S. Anderson, Michelle L. Maclennan, G. Picard, J. Shates, S. Marinsek, Liliana Margonari, Martin Truffer, E. Pettit
Abstract. In late March 2011, landfast sea ice (hereafter, “fast ice”) formed in the northern Larsen B embayment and persisted continuously as multi-year fast ice until January 2022. In the 11 years of fast-ice presence, the northern Larsen B glaciers slowed significantly, thickened in their lower reaches, and developed extensive mélange areas, leading to the formation of ice tongues that extended up to 16 km from the 2011 ice fronts. In situ measurements of ice speed on adjacent ice shelf areas spanning 2011 to 2017 show that the fast ice provided significant resistive stress to ice flow. Fast-ice breakout began in late January 2022 and was closely followed by retreat and breakup of both the fast-ice mélange and the glacier ice tongues. We investigate the probable triggers for the loss of fast ice and document the initial upstream glacier responses. The fast-ice breakup is linked to the arrival of a strong ocean swell event (>1.5 m amplitude; wave period waves >5 s) originating from the northeast. Wave propagation to the ice front was facilitated by a 12-year low in sea ice concentration in the northwestern Weddell Sea, creating a near-ice-free corridor to the open ocean. Remote sensing data in the months following the fast-ice breakout reveals an initial ice flow speed increase (>2-fold), elevation loss (9 to 11 m), and rapid calving of floating and grounded ice for the three main embayment glaciers Crane (11 km), Hektoria (25 km), and Green (18 km).
{"title":"Triggers of the 2022 Larsen B multi-year landfast sea ice breakout and initial glacier response","authors":"Naomi E. Ochwat, Ted A. Scambos, Alison F. Banwell, Robert S. Anderson, Michelle L. Maclennan, G. Picard, J. Shates, S. Marinsek, Liliana Margonari, Martin Truffer, E. Pettit","doi":"10.5194/tc-18-1709-2024","DOIUrl":"https://doi.org/10.5194/tc-18-1709-2024","url":null,"abstract":"Abstract. In late March 2011, landfast sea ice (hereafter, “fast ice”) formed in the northern Larsen B embayment and persisted continuously as multi-year fast ice until January 2022. In the 11 years of fast-ice presence, the northern Larsen B glaciers slowed significantly, thickened in their lower reaches, and developed extensive mélange areas, leading to the formation of ice tongues that extended up to 16 km from the 2011 ice fronts. In situ measurements of ice speed on adjacent ice shelf areas spanning 2011 to 2017 show that the fast ice provided significant resistive stress to ice flow. Fast-ice breakout began in late January 2022 and was closely followed by retreat and breakup of both the fast-ice mélange and the glacier ice tongues. We investigate the probable triggers for the loss of fast ice and document the initial upstream glacier responses. The fast-ice breakup is linked to the arrival of a strong ocean swell event (>1.5 m amplitude; wave period waves >5 s) originating from the northeast. Wave propagation to the ice front was facilitated by a 12-year low in sea ice concentration in the northwestern Weddell Sea, creating a near-ice-free corridor to the open ocean. Remote sensing data in the months following the fast-ice breakout reveals an initial ice flow speed increase (>2-fold), elevation loss (9 to 11 m), and rapid calving of floating and grounded ice for the three main embayment glaciers Crane (11 km), Hektoria (25 km), and Green (18 km).\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140717943","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}
W. Haeberli, L. Arenson, Julie Wee, C. Hauck, N. Mölg
Abstract. Viscous-flow features in perennially frozen talus/debris called rock glaciers are being systematically inventoried as part of the global climate-related monitoring of mountain permafrost. In order to avoid duplication and confusion, guidelines were developed by the International Permafrost Association to discriminate between the permafrost-related landform “rock glacier” and the glacier-related landform “debris-covered glacier”. In two regions covered by detailed field measurements, the corresponding data- and physics-based concepts are tested and shown to be adequate. Key physical aspects which cause the striking morphological and dynamic differences between the two phenomena/landforms concern the following: tight mechanical coupling of the surface material to the frozen rock–ice mixture in the case of rock glaciers, contrasting with essential non-coupling of debris to the glaciers they cover; talus-type advancing fronts of rock glaciers exposing fresh debris material from inside the moving frozen bodies, as opposed to massive surface ice exposed by increasingly rare advancing fronts of debris-covered glaciers; and increasing creep rates and continued advance of rock glaciers as convex landforms with structured surfaces versus predominant slowing down and disintegration of debris-covered glaciers as often concave landforms with primarily chaotic surface structure. Where debris-covered surface ice is or has recently been in contact with thermally controlled subsurface ice in permafrost, complex conditions and interactions can develop morphologies beyond simple either–or-type landform classification. In such cases, the remains of buried surface ice mostly tend to be smaller than the lower size limit of “glaciers” as the term is applied in glacier inventories and to be far thinner than the permafrost in which they are embedded.
{"title":"Discriminating viscous-creep features (rock glaciers) in mountain permafrost from debris-covered glaciers – a commented test at the Gruben and Yerba Loca sites, Swiss Alps and Chilean Andes","authors":"W. Haeberli, L. Arenson, Julie Wee, C. Hauck, N. Mölg","doi":"10.5194/tc-18-1669-2024","DOIUrl":"https://doi.org/10.5194/tc-18-1669-2024","url":null,"abstract":"Abstract. Viscous-flow features in perennially frozen talus/debris called rock glaciers are being systematically inventoried as part of the global climate-related monitoring of mountain permafrost. In order to avoid duplication and confusion, guidelines were developed by the International Permafrost Association to discriminate between the permafrost-related landform “rock glacier” and the glacier-related landform “debris-covered glacier”. In two regions covered by detailed field measurements, the corresponding data- and physics-based concepts are tested and shown to be adequate. Key physical aspects which cause the striking morphological and dynamic differences between the two phenomena/landforms concern the following: tight mechanical coupling of the surface material to the frozen rock–ice mixture in the case of rock glaciers, contrasting with essential non-coupling of debris to the glaciers they cover; talus-type advancing fronts of rock glaciers exposing fresh debris material from inside the moving frozen bodies, as opposed to massive surface ice exposed by increasingly rare advancing fronts of debris-covered glaciers; and increasing creep rates and continued advance of rock glaciers as convex landforms with structured surfaces versus predominant slowing down and disintegration of debris-covered glaciers as often concave landforms with primarily chaotic surface structure. Where debris-covered surface ice is or has recently been in contact with thermally controlled subsurface ice in permafrost, complex conditions and interactions can develop morphologies beyond simple either–or-type landform classification. In such cases, the remains of buried surface ice mostly tend to be smaller than the lower size limit of “glaciers” as the term is applied in glacier inventories and to be far thinner than the permafrost in which they are embedded.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"10 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140722871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Despite being one of the most fundamental microstructural parameters of snow, the specific surface area (SSA) dynamics during temperature gradient metamorphism (TGM) have so far been addressed only within empirical modeling. To surpass this limitation, we propose a rigorous modeling of SSA dynamics using an exact equation for the temporal evolution of the surface area, fed by pore-scale finite-element simulations of the water vapor field coupled with the temperature field on X-ray computed tomography images. The proposed methodology is derived from the first principles of physics and thus does not rely on any empirical parameter. Since the calculated evolution of the SSA is highly sensitive to fluctuations in the experimental data, we quantify the impact of these fluctuations within a stochastic error model. In our simulations, the only poorly constrained physical parameter is the condensation coefficient α. We address this problem by simulating the SSA evolution for a wide range of α values and estimate optimal values by minimizing the differences between simulations and experiments. This methodology suggests that α lies in the intermediate range 10-3<α<10-1 and slightly varies between experiments. Also, our results suggest a transition of the value of α in one TGM experiment, which can be explained by a transition in the underlying surface morphology. Overall, we are able to reproduce very subtle variations in the SSA evolution with correlations of R2=0.95 and 0.99, respectively, for the two TGM time series considered. Finally, our work highlights the necessity of including kinetic effects and of using realistic microstructures to comprehend the evolution of SSA during TGM.
{"title":"A rigorous approach to the specific surface area evolution in snow during temperature gradient metamorphism","authors":"Anna Braun, Kévin Fourteau, Henning Löwe","doi":"10.5194/tc-18-1653-2024","DOIUrl":"https://doi.org/10.5194/tc-18-1653-2024","url":null,"abstract":"Abstract. Despite being one of the most fundamental microstructural parameters of snow, the specific surface area (SSA) dynamics during temperature gradient metamorphism (TGM) have so far been addressed only within empirical modeling. To surpass this limitation, we propose a rigorous modeling of SSA dynamics using an exact equation for the temporal evolution of the surface area, fed by pore-scale finite-element simulations of the water vapor field coupled with the temperature field on X-ray computed tomography images. The proposed methodology is derived from the first principles of physics and thus does not rely on any empirical parameter. Since the calculated evolution of the SSA is highly sensitive to fluctuations in the experimental data, we quantify the impact of these fluctuations within a stochastic error model. In our simulations, the only poorly constrained physical parameter is the condensation coefficient α. We address this problem by simulating the SSA evolution for a wide range of α values and estimate optimal values by minimizing the differences between simulations and experiments. This methodology suggests that α lies in the intermediate range 10-3<α<10-1 and slightly varies between experiments. Also, our results suggest a transition of the value of α in one TGM experiment, which can be explained by a transition in the underlying surface morphology. Overall, we are able to reproduce very subtle variations in the SSA evolution with correlations of R2=0.95 and 0.99, respectively, for the two TGM time series considered. Finally, our work highlights the necessity of including kinetic effects and of using realistic microstructures to comprehend the evolution of SSA during TGM.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"18 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140726658","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}
H. Machguth, A. Eichler, M. Schwikowski, Sabina Brütsch, E. Mattea, S. Kutuzov, Martin Heule, R. Usubaliev, Sultan Belekov, V. Mikhalenko, M. Hoelzle, M. Kronenberg
Abstract. Grigoriev ice cap, located in the Tien Shan mountains of Kyrgyzstan, has a rich history of firn and ice core drilling starting as early as 1962. Here we extend the exceptional record and describe an 18 m firn core, drilled in February 2018 on the summit of Grigoriev ice cap, at 4600 m a.s.l. The core has been analyzed for firn stratigraphy, major ions, black carbon, water stable isotope ratios and total β activity. We find that the core covers 46±3 years and overlaps by 2 to 3 decades with legacy cores. A good agreement is found in major ion concentrations for the overlapping period. Concentrations of black carbon and major ions are reduced since the early 2000s, indicating the onset of meltwater runoff. Nevertheless, general concentration trends of these species are consistent with observations and Central Asian ice core records, since emissions were highest during periods when melt influence was negligible. The record of water stable isotopes does not reflect the strong increase of air temperatures during the last decades, implying that water stable isotope ratios ceased to be proxies of temperature variations at this site. Apart from runoff evidence, however, the firn's thermal regime appears remarkably unchanged. Firn temperatures in 2018 were the highest on record (∼-1.6 °C at ∼17 m depth). However, temperatures in 2023 are again similar to the early 2000s at ∼-2.5 °C. Furthermore, we find little change in net accumulation since the 1980s. We hypothesize (i) that firn temperatures are stabilized by the removal of latent heat through lateral meltwater runoff, and (ii) that mass loss by runoff is compensated by an increase in accumulation. Data from a nearby weather station support the latter hypothesis.
{"title":"Fifty years of firn evolution on Grigoriev ice cap, Tien Shan, Kyrgyzstan","authors":"H. Machguth, A. Eichler, M. Schwikowski, Sabina Brütsch, E. Mattea, S. Kutuzov, Martin Heule, R. Usubaliev, Sultan Belekov, V. Mikhalenko, M. Hoelzle, M. Kronenberg","doi":"10.5194/tc-18-1633-2024","DOIUrl":"https://doi.org/10.5194/tc-18-1633-2024","url":null,"abstract":"Abstract. Grigoriev ice cap, located in the Tien Shan mountains of Kyrgyzstan, has a rich history of firn and ice core drilling starting as early as 1962. Here we extend the exceptional record and describe an 18 m firn core, drilled in February 2018 on the summit of Grigoriev ice cap, at 4600 m a.s.l. The core has been analyzed for firn stratigraphy, major ions, black carbon, water stable isotope ratios and total β activity. We find that the core covers 46±3 years and overlaps by 2 to 3 decades with legacy cores. A good agreement is found in major ion concentrations for the overlapping period. Concentrations of black carbon and major ions are reduced since the early 2000s, indicating the onset of meltwater runoff. Nevertheless, general concentration trends of these species are consistent with observations and Central Asian ice core records, since emissions were highest during periods when melt influence was negligible. The record of water stable isotopes does not reflect the strong increase of air temperatures during the last decades, implying that water stable isotope ratios ceased to be proxies of temperature variations at this site. Apart from runoff evidence, however, the firn's thermal regime appears remarkably unchanged. Firn temperatures in 2018 were the highest on record (∼-1.6 °C at ∼17 m depth). However, temperatures in 2023 are again similar to the early 2000s at ∼-2.5 °C. Furthermore, we find little change in net accumulation since the 1980s. We hypothesize (i) that firn temperatures are stabilized by the removal of latent heat through lateral meltwater runoff, and (ii) that mass loss by runoff is compensated by an increase in accumulation. Data from a nearby weather station support the latter hypothesis.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"108 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140726011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. D. Moro, A. Sperrevik, T. Lavergne, Laurent Bertino, Y. Gusdal, S. C. Iversen, Jozef Rusin, M. D. Moro
Abstract. Operational forecasting systems routinely assimilate daily means of sea ice concentration (SIC) from microwave radiometers in order to improve the accuracy of the forecasts. However, the temporal and spatial averaging of the individual satellite swaths into daily means of SIC entails two main drawbacks: (i) the spatial resolution of the original product is blurred (especially critical in periods with strong sub-daily sea ice movement), and (ii) the sub-daily frequency of passive microwave observations in the Arctic are not used, providing less temporal resolution in the data assimilation (DA) analysis and, therefore, in the forecast. Within the SIRANO (Sea Ice Retrievals and data Assimilation in NOrway) project, we investigate how challenges (i) and (ii) can be avoided by assimilating individual satellite swaths (level 3 uncollated) instead of daily means (level 3) of SIC. To do so, we use a regional configuration of the Barents Sea (2.5 km grid) based on the Regional Ocean Modeling System (ROMS) and the Los Alamos Sea Ice Model (CICE) together with the ensemble Kalman filter (EnKF) as the DA system. The assimilation of individual swaths significantly improves the EnKF analysis of SIC compared to the assimilation of daily means; the mean absolute difference (MAD) shows a 10 % improvement at the end of the assimilation period and a 7 % improvement at the end of the 7 d forecast period. This improvement is caused by better exploitation of the information provided by the SIC swath data, in terms of both spatial and temporal variance, compared to the case when the swaths are combined to form a daily mean before assimilation.
摘要。业务预报系统通常会吸收微波辐射计的海冰浓度（SIC）日均值，以提高预报的准确性。然而，将单个卫星扫面的时空平均值转化为海冰浓度日均值有两个主要缺点：(i) 原始产品的空间分辨率模糊（在海冰次日运动强烈的时期尤为关键），(ii) 北极地区被动微波观测的次日频率未被使用，从而降低了数据同化分析的时间分辨率，因此也降低了预报的时间分辨率。在 SIRANO（NOrway 中的海冰检索和数据同化）项目中，我们研究了如何通过同化单个卫星扫面（第 3 级无ollated）而不是 SIC 的日均值（第 3 级）来避免挑战（i）和（ii）。为此，我们使用了基于区域海洋模拟系统（ROMS）和洛斯阿拉莫斯海冰模式（CICE）的巴伦支海区域配置（2.5 公里网格），并使用集合卡尔曼滤波器（EnKF）作为数据分析系统。与日均值同化相比，单个扇面同化显著提高了 EnKF 对 SIC 的分析能力；平均绝对差值（MAD）显示，同化期结束时提高了 10%，7 天预报期结束时提高了 7%。与同化前将各切面合并成日平均值的情况相比，这种改进是由于更好地利用了 SIC 切面数据在空间和时间方差方面提供的信息。
{"title":"Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model","authors":"M. D. Moro, A. Sperrevik, T. Lavergne, Laurent Bertino, Y. Gusdal, S. C. Iversen, Jozef Rusin, M. D. Moro","doi":"10.5194/tc-18-1597-2024","DOIUrl":"https://doi.org/10.5194/tc-18-1597-2024","url":null,"abstract":"Abstract. Operational forecasting systems routinely assimilate daily means of sea ice concentration (SIC) from microwave radiometers in order to improve the accuracy of the forecasts. However, the temporal and spatial averaging of the individual satellite swaths into daily means of SIC entails two main drawbacks: (i) the spatial resolution of the original product is blurred (especially critical in periods with strong sub-daily sea ice movement), and (ii) the sub-daily frequency of passive microwave observations in the Arctic are not used, providing less temporal resolution in the data assimilation (DA) analysis and, therefore, in the forecast. Within the SIRANO (Sea Ice Retrievals and data Assimilation in NOrway) project, we investigate how challenges (i) and (ii) can be avoided by assimilating individual satellite swaths (level 3 uncollated) instead of daily means (level 3) of SIC. To do so, we use a regional configuration of the Barents Sea (2.5 km grid) based on the Regional Ocean Modeling System (ROMS) and the Los Alamos Sea Ice Model (CICE) together with the ensemble Kalman filter (EnKF) as the DA system. The assimilation of individual swaths significantly improves the EnKF analysis of SIC compared to the assimilation of daily means; the mean absolute difference (MAD) shows a 10 % improvement at the end of the assimilation period and a 7 % improvement at the end of the 7 d forecast period. This improvement is caused by better exploitation of the information provided by the SIC swath data, in terms of both spatial and temporal variance, compared to the case when the swaths are combined to form a daily mean before assimilation.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"87 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140729283","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}
Xinwei Chen, Muhammed Patel, Fernando J. Pena Cantu, Jinman Park, Javier Noa Turnes, Linlin Xu, K. A. Scott, David A Clausi
Abstract. The AutoICE challenge, organized by multiple national and international agencies, seeks to advance the development of near-real-time sea ice products with improved spatial resolution, broader spatial and temporal coverage, and enhanced consistency. In this paper, we present a detailed description of our solutions and experimental results for the challenge. We have implemented an automated sea ice mapping pipeline based on a multi-task U-Net architecture, capable of predicting sea ice concentration (SIC), stage of development (SOD), and floe size (FLOE). The AI4Arctic dataset, which includes synthetic aperture radar (SAR) imagery, ancillary data, and ice-chart-derived label maps, is utilized for model training and evaluation. Among the submissions from over 30 teams worldwide, our team achieved the highest combined score of 86.3 %, as well as the highest scores on SIC (92.0 %) and SOD (88.6 %). Notably, the result analysis and ablation studies demonstrate that instead of model architecture design, a collection of strategies/techniques we employed led to substantial enhancement in accuracy, efficiency, and robustness within the realm of deep-learning-based sea ice mapping. Those techniques include input SAR variable downscaling, input feature selection, spatial–temporal encoding, and the choice of loss functions. By highlighting the various techniques employed and their impacts, we aim to underscore the scientific advancements achieved in our methodology.
{"title":"MMSeaIce: a collection of techniques for improving sea ice mapping with a multi-task model","authors":"Xinwei Chen, Muhammed Patel, Fernando J. Pena Cantu, Jinman Park, Javier Noa Turnes, Linlin Xu, K. A. Scott, David A Clausi","doi":"10.5194/tc-18-1621-2024","DOIUrl":"https://doi.org/10.5194/tc-18-1621-2024","url":null,"abstract":"Abstract. The AutoICE challenge, organized by multiple national and international agencies, seeks to advance the development of near-real-time sea ice products with improved spatial resolution, broader spatial and temporal coverage, and enhanced consistency. In this paper, we present a detailed description of our solutions and experimental results for the challenge. We have implemented an automated sea ice mapping pipeline based on a multi-task U-Net architecture, capable of predicting sea ice concentration (SIC), stage of development (SOD), and floe size (FLOE). The AI4Arctic dataset, which includes synthetic aperture radar (SAR) imagery, ancillary data, and ice-chart-derived label maps, is utilized for model training and evaluation. Among the submissions from over 30 teams worldwide, our team achieved the highest combined score of 86.3 %, as well as the highest scores on SIC (92.0 %) and SOD (88.6 %). Notably, the result analysis and ablation studies demonstrate that instead of model architecture design, a collection of strategies/techniques we employed led to substantial enhancement in accuracy, efficiency, and robustness within the realm of deep-learning-based sea ice mapping. Those techniques include input SAR variable downscaling, input feature selection, spatial–temporal encoding, and the choice of loss functions. By highlighting the various techniques employed and their impacts, we aim to underscore the scientific advancements achieved in our methodology.\u0000","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"271 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140730316","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：