Physical properties and spatial distribution of the sea ice surface layer (SSL/snow) during the autumn phase of the MOSAiC expedition

2021 ◽  
Author(s):  
Ruzica Dadic ◽  
Martin Schneebeli ◽  
Henna-Reeta Hannula ◽  
Amy Macfarlane ◽  
Roberta Pirazzini
Keyword(s):  
Surface Layer ◽  
Spatial Distribution ◽  
Sea Ice ◽  
Physical Properties ◽  
Surface Scattering ◽  
Climate Feedback ◽  
Energy Fluxes ◽  
Scattering Layer ◽  
Near Surface ◽  
Ice Surface

<p>Snow cover dominates the thermal and optical properties of sea ice and the energy fluxes between the ocean and the atmosphere, yet data on the physical properties of snow and its effects on sea ice are limited. This lack of data leads to two significant problems: 1) significant biases in model representations of the sea ice cover and the processes that drive it, and 2) large uncertainties in how sea ice influences the global energy budget and the coupling of climate feedback. The  MOSAiC research initiative enabled the most extensive data collection of snow and surface scattering layer (SSL) properties over sea ice to date. During leg 5 of the MOSAiC expedition, we collected multi-scale (microscale to 100-m scale) measurements of the surface layer (snow/SSL) over first year ice (FYI) and MYI on a daily basis. The ultimate goal of our measurements is to determine the spatial distribution of physical properties of the surface layer. During leg 5 of the MOSAiC expedition, that surface layer changed from the  surface scattering layer (SSL),   characteristic for the melt season, to an early autumn snow pack. Here,  we will present data showing both a) the physical properties and the spatial distribution of the SSL during the late melt season and b) the transition of the sea ice surface from the SSL to the fresh autumn snowpack. The structural properties of this transition period are poorly documented, and this season is critical  for the initialization of sea ice and snow models. Furthermore, these data are crucial to interpret simultaneous observations of surface energy fluxes, surface optical and remote sensing data (microwave signals in particular), near-surface biochemical activity, and to understand the sea ice  processes that occur as the sea ice transitions from melting to freezing.</p>

scholarly journals Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison for Arctic Summer Conditions

2020 ◽  
Author(s):  
Stefan Kern ◽  
Thomas Lavergne ◽  
Dirk Notz ◽  
Leif Toudal Pedersen ◽  
Rasmus Tage Tonboe
Keyword(s):  
Spatial Distribution ◽  
Sea Ice ◽  
Passive Microwave ◽  
Sea Ice Concentration ◽  
Group Iii ◽  
Melt Pond ◽  
Ice Surface ◽  
Group I ◽  
Ice Concentration ◽  
Group Ii

Abstract. We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from satellite passive microwave (PMW) observations for the Arctic during summer. The products are compared against SIC and net ice-surface fraction (ISF) – SIC minus the per-grid cell melt-pond fraction (MPF) on sea ice – as derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations and observed from ice-going vessels. Like in Kern et al. (2019), we group the 10 products based on the concept of the SIC retrieval used. Group I consists of products of the EUMETSAT OSI SAF and ESA CCI algorithms. Group II consists of products derived with the Comiso bootstrap algorithm and the NOAA NSIDC SIC climate data record (CDR). Group III consists of ARTIST Sea Ice (ASI) and NASA Team (NT) algorithm products and group IV consists of products of the enhanced NASA Team algorithm (NT2). We find wide-spread positive and negative differences between PMW and MODIS SIC with magnitudes frequently reaching up to 20–25 % for groups I and III and up to 30–35 % for groups II and IV. On a pan-Arctic scale these differences may cancel out: Arctic average SIC from Group I products agrees with MODIS within 2–5 % accuracy during the entire melt period from May through September. Group II and IV products over-estimate MODIS Arctic average SIC by 5–10 %. Out of group III, ASI is similar to group I products while NT SIC under-estimates MODIS Arctic average SIC by 5–10 %. These differences, when translated into the impact computing Arctic sea-ice area (SIA), match well with the differences in SIA between the four groups reported for the summer months by Kern et al. (2019). MODIS ISF is systematically over-estimated by all products; NT provides the smallest (up to 25 %) over-estimations, group II and IV products the largest (up to 45 %) over-estimations. The spatial distribution of the observed over-estimation of MODIS ISF agrees reasonably well with the spatial distribution of the MODIS MPF and we find a robust linear relationship between PMW SIC and MODIS ISF for group I and III products during peak melt, i.e. July and August. We discuss different cases taking into account the expected influence of ice-surface properties other than melt ponds, i.e. wet snow and coarse grained snow / refrozen surface, on PMW observations used in the SIC retrieval algorithms. Based on this discussion we identify the mismatch between the actually observed surface properties and those represented by the ice tie points as the most likely reason for i) the observed differences between PMW SIC and MODIS ISF and for ii) the often surprisingly small difference between PMW and MODIS SIC in areas of high melt-pond fraction. We conclude that all 10 SIC products are highly inaccurate during summer melt. We hypothesize that the un-known amount of melt-pond signatures likely included in the ice tie points plays an important role – particularly for groups I and II – and suggest to conduct further research in this field.

scholarly journals Geostatistical FDEM inversion: a three-dimensional real case application

2021 ◽  
Author(s):  
Leonardo Azevedo ◽  
João Narciso ◽  
Ellen Van De Vijver
Keyword(s):  
Spatial Distribution ◽  
Physical Properties ◽  
Three Dimensional ◽  
Real Case ◽  
Small Scale ◽  
Inversion Method ◽  
Two Dimensional ◽  
Data Set ◽  
Near Surface ◽  
Direct Observations

<p>The near surface is a complex and often highly heterogeneous system as its current status results from interacting processes of both natural and anthropogenic origin. Effective sustainable management and land use planning, especially in urban environments, demands high-resolution subsurface property models enabling to capture small-scale processes of interest. The modelling methods based only on discrete direct observations from conventional invasive sampling techniques have limitations with respect to capturing the spatial variability of these systems. Near-surface geophysical surveys are emerging as powerful techniques to provide indirect measurements of subsurface properties. Their integration with direct observations has the potential for better predicting the spatial distribution of the subsurface physical properties of interest and capture the heterogeneities of the near-surface systems.</p><p>Within the most common geophysical techniques, frequency-domain electromagnetic (FDEM) induction methods have demonstrated their potential and efficiency to characterize heterogeneous deposits due to their simultaneous sensitivity to electrical conductivity (EC) and magnetic susceptibility (MS). The inverse modelling of FDEM data based on geostatistical techniques allows to go beyond conventional analyses of FDEM data. This geostatistical FDEM inversion method uses stochastic sequential simulation and co-simulation to perturbate the model parameter space and the corresponding FDEM forward model solutions, including both the synthetic FDEM responses and their sensitivity to changes on the physical properties of interest. A stochastic optimization driven by the misfit between true and synthetic FDEM data is applied to iterative towards a final subsurface model. This method not only improve the confidence of the obtained EC and MS inverted models but also allows to quantify the uncertainty related to them. Furthermore, taking into account spatial correlations enables more accurate prediction of the spatial distribution of subsurface properties and a more realistic reconstruction of small-scale spatial variations, even when considering highly heterogeneous near surface systems. Moreover, a main advantage of this iterative geostatistical FDEM inversion method is its ability to flexibly integrate data with different resolution in the same framework.</p><p>In this work, we apply this iterative geostatistical FDEM inversion technique, which has already been successfully demonstrated for one- and two-dimensional applications, to invert a real case FDEM data set in three dimensions. The FDEM survey data set was collected on a site located near Knowlton (Dorset, UK), which is geologically characterized by Cretaceous chalk overlain by Quaternary siliciclastic sand deposits. The subsurface at the site is known to contain several archaeological features, which produces strong local in-phase anomalies in the FDEM survey data. We discuss the particular challenges involved in the three-dimensional application of the inversion method to a real case data set and compare our results against previously obtained ones for one- and two-dimensional approximations.</p>

scholarly journals Satellite passive microwave sea-ice concentration data set inter-comparison for Arctic summer conditions

2020 ◽  
Vol 14 (7) ◽  
pp. 2469-2493 ◽  
Author(s):  
Stefan Kern ◽  
Thomas Lavergne ◽  
Dirk Notz ◽  
Leif Toudal Pedersen ◽  
Rasmus Tonboe
Keyword(s):  
Spatial Distribution ◽  
Sea Ice ◽  
Passive Microwave ◽  
Sea Ice Concentration ◽  
Group Iii ◽  
Melt Pond ◽  
Ice Surface ◽  
Group I ◽  
Ice Concentration ◽  
Group Ii

Abstract. We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from satellite passive microwave (PMW) observations for the Arctic during summer. The products are compared against SIC and net ice surface fraction (ISF) – SIC minus the per-grid-cell melt pond fraction (MPF) on sea ice – as derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations and observed from ice-going vessels. Like in Kern et al. (2019), we group the 10 products based on the concept of the SIC retrieval used. Group I consists of products of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) and European Space Agency (ESA) Climate Change Initiative (CCI) algorithms. Group II consists of products derived with the Comiso bootstrap algorithm and the National Oceanographic and Atmospheric Administration (NOAA) National Snow and Ice Data Center (NSIDC) SIC climate data record (CDR). Group III consists of Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) and National Aeronautics and Space Administration (NASA) Team (NT) algorithm products, and group IV consists of products of the enhanced NASA Team algorithm (NT2). We find widespread positive and negative differences between PMW and MODIS SIC with magnitudes frequently reaching up to 20 %–25 % for groups I and III and up to 30 %–35 % for groups II and IV. On a pan-Arctic scale these differences may cancel out: Arctic average SIC from group I products agrees with MODIS within 2 %–5 % accuracy during the entire melt period from May through September. Group II and IV products overestimate MODIS Arctic average SIC by 5 %–10 %. Out of group III, ASI is similar to group I products while NT SIC underestimates MODIS Arctic average SIC by 5 %–10 %. These differences, when translated into the impact computing Arctic sea-ice area (SIA), match well with the differences in SIA between the four groups reported for the summer months by Kern et al. (2019). MODIS ISF is systematically overestimated by all products; NT provides the smallest overestimations (up to 25 %) and group II and IV products the largest overestimations (up to 45 %). The spatial distribution of the observed overestimation of MODIS ISF agrees reasonably well with the spatial distribution of the MODIS MPF and we find a robust linear relationship between PMW SIC and MODIS ISF for group I and III products during peak melt, i.e. July and August. We discuss different cases taking into account the expected influence of ice surface properties other than melt ponds, i.e. wet snow and coarse-grained snow/refrozen surface, on brightness temperatures and their ratios used as input to the SIC retrieval algorithms. Based on this discussion we identify the mismatch between the actually observed surface properties and those represented by the ice tie points as the most likely reason for (i) the observed differences between PMW SIC and MODIS ISF and for (ii) the often surprisingly small difference between PMW and MODIS SIC in areas of high melt pond fraction. We conclude that all 10 SIC products are highly inaccurate during summer melt. We hypothesize that the unknown number of melt pond signatures likely included in the ice tie points plays an important role – particularly for groups I and II – and recommend conducting further research in this field.

Observing and Modeling the Surface Scattering Layer of First-Year Arctic Sea Ice

2007 ◽  
Author(s):  
Richard Moritz ◽  
Bonnie Light
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Surface Scattering ◽  
First Year ◽  
Scattering Layer ◽  
Arctic Sea

scholarly journals Radiometrie measurements of sea-ice surface temperature in East Antarctica

1998 ◽  
Vol 27 ◽  
pp. 466-470
Author(s):  
Kelvin J. Michael ◽  
Clemente S. Hungria ◽  
R. A. Massom
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Snow Cover ◽  
Open Water ◽  
Thermal Infrared ◽  
Near Surface ◽  
Video Footage ◽  
Antarctic Sea Ice ◽  
Ice Surface ◽  
Ice Conditions

This paper presents surface temperature data collected over East Antarctic sea ice by two thermal infrared radiometers mounted on the RSV Aurora Australis in March-May 1993. Operating at wavelengths equivalent to those utilised by channels 4 and 5 of AVHRR and similar channels of ATSR, the radiometers provided high-reso-lution data on surface (skin) temperature along the ship track. Additional information on the sea-ice conditions was obtained from hourly observations made from The ship's bridge, video footage and direct measurements made at ice stations. Following calibration, time series of temperatures from each of the radiometers were compared wi th ice-surface and near-surface air temperatures. Observed changes in the surface temperature are related to different snow and ice conditions. For a given air temperature, the surface temperature depends upon the thickness of ice and its snow cover. While open water areas (leads) have temperatures near -2.0°C, thick ice is characterised by surface temperatures which approximate those of the air. Taken as a whole, the along-track profile of surface temperature provides a proxy estimate of The proportion of open water and thin ice with in the pack. The presence of a snow cover has a significant effect on the surface temperature. It is anticipated that the results will be of use in the validation of sea-ice models and satellite thermal infrared data.

Spatial Distribution of the Dislocation Trails in Silicon

2015 ◽  
Vol 242 ◽  
pp. 155-159 ◽  
Author(s):  
V.I. Orlov ◽  
E.B. Yakimov ◽  
Nikolai Yarykin
Keyword(s):  
Surface Layer ◽  
Spatial Distribution ◽  
Fine Structure ◽  
Chemical Etching ◽  
Middle Part ◽  
Recombination Activity ◽  
Near Surface ◽  
Half Loop ◽  
Selective Chemical ◽  
Selective Chemical Etching

Formation of the dislocation trails (DTs) left at the slip plane behind expanding dislocation half-loops is studied in Cz-Si plastically deformed at 600°C using the selective chemical etching and the EBIC and LBIC techniques which are sensitive to the defect recombination activity. It is found that the dislocation trails are qualitatively different for the half-loops expanded from the tensile and compressed surfaces of the bent sample. In the tensile part, DTs with the strongest recombination contrast are always revealed behind the 60oarm of the half-loops, while DTs are invisible (if exits at all) behind another arm and the bottom segment. The dissimilar behavior of two differently aligned 60° segments can be related to a different fine structure of their cores. The picture of dislocation trails is found to be different in the 1020 μm surface layer that is tentatively ascribed to the near-surface bending of dislocations. In the compressed part, the EBIC and LBIC contrasts are generally smaller and the noticeable DTs are only revealed in the middle part the half-loop. Presumable reasons for the effects are discussed.

scholarly journals Temporal evolution of physical and dielectric properties of sea ice and snow during the early melt season: observations from SIMS ’90 experiment

1994 ◽  
Vol 40 (134) ◽  
pp. 16-30 ◽  
Author(s):  
Mohammede.E Shokr ◽  
David G. Barber
Keyword(s):  
Surface Layer ◽  
Dielectric Properties ◽  
Sea Ice ◽  
Temporal Evolution ◽  
Daily Basis ◽  
Complex Dielectric Constant ◽  
Late Winter ◽  
First Year ◽  
Monitoring Site ◽  
Ice Surface

AbstractThe first field experiment in the 5 year seasonal Sea Ice Monitoring Site (SIMS) program was conducted in Resolute Passage, Canadian Eastern Arctic, between 15 May and 8 June 1990. This period signals the early melt season of sea ice in that region. A standard array of ice and snow measurements was collected on a daily basis from first-year and multi-year ice to monitor temporal evolution. Measurements included ice salinity, ice temperature and ice-surface roughness, snow salinity, snow temperature, snow density and snow depth. The complex dielectric constant of sea ice was computed from these measurements. Rapid desalination of first-year ice was noticed in the surface layer. Towards the end of the experiment period, salinities of the snow-hoar layer were higher than those of the ice-surface layer. Variation in air temperature is replicated by ice-surface temperature but not by the salinity or dielectric properties. No temporal variation in permittivity and dielectric loss was observed for first-year ice, but a slight increase in both parameters was observed for multi-year ice. As a result, a slight decrease in the microwave-penetration depth was observed for multi-year ice. Physical properties of ice and snow were compared against results obtained from other experiments conducted in different ice-formation regions in the late winter and in the early melt season.

scholarly journals Пространственное распределение каналируемых ионов и пробеги изотопов водорода в кристаллическом кремнии и вольфраме

2020 ◽  
Vol 46 (5) ◽  
pp. 34
Author(s):  
Д.С. Мелузова ◽  
П.Ю. Бабенко ◽  
А.П. Шергин ◽  
А.Н. Зиновьев
Keyword(s):  
Spatial Distribution ◽  
Angular Distribution ◽  
Spatial Structure ◽  
Short Distance ◽  
Depth Distribution ◽  
The Other ◽  
Surface Scattering ◽  
Near Surface ◽  
Adjacent Channels

Ranges of H and D ions in crystalline Si and W were calculated. It is shown, that as the energy of ions increases the depth distribution of ranges splits into two components: one is connected with near-surface scattering, and the other one characterizes channeled particles. A new phenomenon was observed – a stable spatial structure of the channeled part of the beam forms after the beam passes a short distance. As the ions slow down they start to transition into adjacent channels. The spatial structure of channeled ions falls apart near the stopping point of the particles. An experiment which would connect the obtained spatial distribution with angular distribution of emitted particles is proposed.

scholarly journals Geostatistical inversion of electromagnetic induction data for landfill modelling

2020 ◽  
Author(s):  
João Narciso ◽  
Leonardo Azevedo ◽  
Marc Van Meirvenne ◽  
Ellen Van De Vijver
Keyword(s):  
Spatial Distribution ◽  
Physical Properties ◽  
Water Saturation ◽  
Raw Materials ◽  
Borehole Data ◽  
Near Surface ◽  
Spatial Continuity ◽  
Simultaneous Inversion ◽  
Geophysical Surveys ◽  
Geostatistical Inversion

<p>The characterization and monitoring of landfills has become a major concern, not only for assessing the associated environmental impact (e.g., groundwater contamination) but also for evaluating the potential for recovery of secondary resources, in particular for the production of raw materials and energy. For both objectives, it is crucial to have knowledge of the waste composition and the current landfill conditions (e.g. water saturation level). Near-surface geophysical surveys have been proven effective for the non-invasive investigation of landfills, in which different methods have been used depending on the specific survey targets.  Because of its sensitivity to two subsurface physical properties, electrical conductivity (EC) and magnetic susceptibility (MS), frequency-domain electromagnetic (FDEM) induction has been successfully applied to the qualitative characterization of urban and industrial landfills, including mine tailings. Yet, due to the generally complex composition and strongly heterogeneous spatial distribution of waste deposits, reconstructing a reliable landfill model from surface geophysical measurements remains challenging. Geostatistical inversion emerges as powerful tool to improve the landfill modelling from geophysical data, allowing for a more detailed description of the spatial distribution of the properties of interest and the associated uncertainty. Additionally, it provides a flexible framework for integrating data from geophysical surveys and conventional sampling from drilling or trenching.</p><p>In this work, we present a new geostatistical inversion technique able for the simultaneous inversion of FDEM data for EC and MS, which optimize the landfill modelling procedure and is sensitive towards change on the physical properties of interest. This method is based on an iterative procedure where ensembles of subsurface models of EC and MS are generated with stochastic sequential simulation and co-simulation. These simulated models are conditioned locally by existing borehole data for these properties and by a spatial continuity pattern imposed by a variogram model. Synthetic instrument response data, including both the in-phase and quadrature-phase components of the FDEM response, are generated from each model using a forward model connecting the data domain (FDEM data) with the model domain (subsurface physical properties). The misfit between the observed and forward-modelled FDEM data, weighted according to the depth sensitivity of the FDEM response toward changes in EC and MS, is used to drive the generation of a new set of models in the next iteration. We illustrate the inversion procedure with synthetic landfill example data sets which were created based on real data collected at a mine tailing in Portugal and a municipal solid waste landfill in Belgium.</p>

scholarly journals Temporal evolution of physical and dielectric properties of sea ice and snow during the early melt season: observations from SIMS ’90 experiment

1994 ◽  
Vol 40 (134) ◽  
pp. 16-30 ◽  
Author(s):  
Mohammede.E Shokr ◽  
David G. Barber
Keyword(s):  
Surface Layer ◽  
Dielectric Properties ◽  
Sea Ice ◽  
Temporal Evolution ◽  
Daily Basis ◽  
Complex Dielectric Constant ◽  
Late Winter ◽  
First Year ◽  
Monitoring Site ◽  
Ice Surface

AbstractThe first field experiment in the 5 year seasonal Sea Ice Monitoring Site (SIMS) program was conducted in Resolute Passage, Canadian Eastern Arctic, between 15 May and 8 June 1990. This period signals the early melt season of sea ice in that region. A standard array of ice and snow measurements was collected on a daily basis from first-year and multi-year ice to monitor temporal evolution. Measurements included ice salinity, ice temperature and ice-surface roughness, snow salinity, snow temperature, snow density and snow depth. The complex dielectric constant of sea ice was computed from these measurements. Rapid desalination of first-year ice was noticed in the surface layer. Towards the end of the experiment period, salinities of the snow-hoar layer were higher than those of the ice-surface layer. Variation in air temperature is replicated by ice-surface temperature but not by the salinity or dielectric properties. No temporal variation in permittivity and dielectric loss was observed for first-year ice, but a slight increase in both parameters was observed for multi-year ice. As a result, a slight decrease in the microwave-penetration depth was observed for multi-year ice. Physical properties of ice and snow were compared against results obtained from other experiments conducted in different ice-formation regions in the late winter and in the early melt season.

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