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  • 1. Bokhorst, Stef
    et al.
    Pedersen, Stine Hojlund
    Brucker, Ludovic
    Anisimov, Oleg
    Bjerke, Jarle W.
    Brown, Ross D.
    Ehrich, Dorothee
    Essery, Richard L. H.
    Heilig, Achim
    Ingvander, Susanne
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Johansson, Cecilia
    Johansson, Margareta
    Jonsdottir, Ingibjorg Svala
    Inga, Niila
    Luojus, Kari
    Macelloni, Giovanni
    Mariash, Heather
    McLennan, Donald
    Rosqvist, Gunhild Ninis
    Stockholm University, Faculty of Science, Department of Physical Geography. University of Bergen, Norway.
    Sato, Atsushi
    Savela, Hannele
    Schneebeli, Martin
    Sokolov, Aleksandr
    Sokratov, Sergey A.
    Terzago, Silvia
    Vikhamar-Schuler, Dagrun
    Williamson, Scott
    Qiu, Yubao
    Callaghan, Terry V.
    Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts2016In: Ambio, ISSN 0044-7447, E-ISSN 1654-7209, Vol. 45, no 5, p. 516-537Article, review/survey (Refereed)
    Abstract [en]

    Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region.

  • 2.
    Brown, Ian A.
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Ingvander, Susanne
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Spatial and temporal variations in Antarctic snow particle size identified in AMSR-E 89 GHz dataManuscript (preprint) (Other academic)
    Abstract [en]

    Here we use in situ observations to identify spatio-temporal variations of snow particle size in 89 GHz AMSR-E passive microwave satellite imagery. We have correlated high temporal resolution data daily AMSR-E with reference to high spatial resolution Envisat ASAR images to a validation dataset of snow particle size acquired during the Japanese Swedish Antarctic Expedition (JASE) 2007/2008. We have found strong correlations between the 89 GHz AMSR-E data and two different size parameters: particle length and estimated Specific Surface Area (SSA). These correlations have been used to model the grain size variations over the entire region of interest. The daily AMSR-E data have been used to study the evolution of the snowpack over time revealing a seasonal metamorphosis of snow particles at the coast that is largely absent on the polar plateau. Furthermore, the AMSR-E data may exhibit effects from the passing of coastal weather systems on 3-6 day cycles. These effects penetrate to the polar plateau and may represent the drainage of cold air from the plateau drawn-down by passing coastal weather systems.

  • 3. Fujita, S.
    et al.
    Holmlund, Per
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Andersson, I.
    Brown, Ian
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Enomoto, H.
    Fujii, Y.
    Fujita, K.
    Fukui, K.
    Furukawa, T.
    Hansson, M.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Hara, K.
    Hoshina, Y.
    Igarashi, M.
    Iizuka, Y.
    Imura, S.
    Ingvander, Susanne
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Karlin, Torbjörn
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Motoyama, H.
    Nakazawa, F.
    Oerter, H.
    Sjöberg, L. E.
    Sugiyama, S.
    Surdyk, S.
    Ström, Johan
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
    Uemura, R.
    Wilhelms, F.
    Spatial and temporal variability of snow accumulation rate on the East Antarctic ice divide between Dome Fuji and EPICA DML2011In: The Cryosphere, ISSN 1994-0416, E-ISSN 1994-0424, Vol. 5, no 4, p. 1057-1081Article in journal (Refereed)
    Abstract [en]

    To better understand the spatio-temporal variability of the glaciological environment in Dronning Maud Land (DML), East Antarctica, a 2800-km-long Japanese-Swedish traverse was carried out. The route includes ice divides between two ice-coring sites at Dome Fuji and EPICA DML. We determined the surface mass balance (SMB) averaged over various time scales in the late Holocene based on studies of snow pits and firn cores, in addition to radar data. We find that the large-scale distribution of the SMB depends on the surface elevation and continentality, and that the SMB differs between the windward and leeward sides of ice divides for strong-wind events. We suggest that the SMB is highly influenced by interactions between the large-scale surface topography of ice divides and the wind field of strong-wind events that are often associated with high-precipitation events. Local variations in the SMB are governed by the local surface topography, which is influenced by the bedrock topography. In the eastern part of DML, the accumulation rate in the second half of the 20th century is found to be higher by similar to 15% than averages over longer periods of 722 a or 7.9 ka before AD 2008. A similar increasing trend has been reported for many inland plateau sites in Antarctica with the exception of several sites on the leeward side of the ice divides.

  • 4.
    Ingvander, Susanne
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Snow particle size investigations using digital image analysis - implications for ground observations and remote sensing of snow2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    During the past century climate warming has caused rapid changes in the Cryosphere. This has increased the need to accurately monitor rates of change in snow and ice in remote or sparsely populated areas where environmental observing capacity is limited. Monitoring snow cover requires understanding of the snow pack and the snow surface attributes. Snow particle size is an important parameter for characterization of snow pack properties. The size and shape of the snow particles affects the snow/air-ratio which in turn affect how energy is reflected on the snow surface. This governs the snow pack energy balance by changing the albedo or backscattering properties of the snow. Both the albedo and the snow particle size can be quantified by remote sensing. However, the snow particle size estimated by remote sensing, also called the optically equivalent particle size, represents only an approximation of the true or physical particle size of snow. Thus, there is demand for methods that relate both parameters and help to improve the interpretation of remote sensing data of snow at higher spatial and temporal scales. To address this demand the aim of this dissertation thesis is to improve existing sampling methods of the physical snow particle size to retrieve high-resolution, spatial and temporal data sets for validation of remote sensing data. A field sampling method based on object-oriented analysis of digital images was developed that allows measurements of various snow particle size parameters such as length, width, area, specific surface area and shape. The method generates a continuous snow particle size distribution that supports the detailed statistical characterization of a large number of samples. The results show its possibility to compare data from different existing methods. The sampling method was applied in field sites in Antarctica and in northern Sweden, to characterize the spatial variability in the physical snow particle size and to estimate correlations between various remote sensing products and the observed physical snow particle size. The results of the presented studies show that more detailed measurements of snow particle size in the field at higher temporal and spatial scales can improve the interpretation of active and passive satellite retrieved data.

  • 5.
    Ingvander, Susanne
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Brown, Ian A.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Jansson, Peter
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Holmlund, Per
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Johansson, Cecilia
    Rosqvist, Gunhild
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Method study: Grain size sampling and digital object oriented image analysis for explanation of snow grain size, shape and distributionIn: Journal of Glaciology, ISSN 0022-1430, E-ISSN 1727-5652Article in journal (Refereed)
    Abstract [en]

    We have developed a digital image processing method for snow particle size and shape analysis suitable for quick and reliable analysis in the eld. Snow particle size is an important parameter strongly aecting snow cover albedo from seasonally snow covered areas and ice sheets. It is also important in remote sensing analysis because it influences the reflectance and scattering properties of the snow. Alternatively traditional methods based on visual inspection of samples can be used but they do not yield quantitative data. Our method provides an additional alternative to both simpler and more complex methods by providinga tool that limits the subjective eect of the visual analysis and provides a quantitativeparticle size distribution. The method involves image analysis software and field efficient instrumentation in order to develop a complete process-chain easily implemented under field conditions. The results from the analysis are a two dimensional analysis of particle size, shape and distributions for each sample. The developed method improves snow particle analysis being quantitative, reproducible and applicable for dierent types of eld sites.

  • 6.
    Ingvander, Susanne
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Brown, Ian A.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Jansson, Peter
    Holmlund, Per
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Johansson, Cecilia
    Rosqvist, Gunhild
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Particle Size Sampling and Object-Oriented Image Analysis for Field Investigations of Snow Particle Size, Shape, and Distribution2013In: Arctic, Antarctic and Alpine research, ISSN 1523-0430, E-ISSN 1938-4246, Vol. 45, no 3, p. 330-341Article in journal (Refereed)
    Abstract [en]

    Snow particle size is an important parameter strongly affecting snow cover broadband albedo from seasonally snow covered areas and ice sheets. It is also important in remote sensing analyses because it influences the reflectance and scattering properties of the snow. We have developed a digital image processing method for the capture and analysis of data of snow particle size and shape. The method is suitable for quick and reliable data capture in the field. Traditional methods based on visual inspection of samples have been used but do not yield quantitative data. Our method provides an alternative to both simpler and more complex methods by providing a tool that limits the subjective effect of the visual analysis and provides a quantitative particle size distribution. The method involves image analysis software and field efficient instrumentation in order to develop a complete process-chain easily implemented under field conditions. The output from the analysis is a two-dimensional analysis of particle size, shape, and distributions for each sample. The results of the segmentation process were validated against manual delineation of snow particles. The developed method improves snow particle analysis because it is quantitative, reproducible, and applicable for different types of field sites.

  • 7.
    Ingvander, Susanne
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Brown, Ian
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Jansson, Peter
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Spatial snow grain size variability along the JASE 2007/2008 traverse route in Dronning Maud Land, Antarctica, and its relation to MOA NDSI index, MEDRIS and MODIS sattelite data2010In: Proceedings of ESA Living Planet Symposium: 28 June - 2 July 2010, Bergen, Norway / [ed] H. Lacoste-Francis, Noordwijk: ESA (European Space Agency) , 2010Conference paper (Other academic)
    Abstract [en]

    Snow grain size is an important parameter for determining albedo of the ice sheets and for calibration of optical and microwave remote sensing scattering processes. Snow grain size is a function of the local climate determined by moisture content, air and snow temperature, their gradients within the snow and firn, and wind patterns. Furthermore, it is an indicator on snow metamorphism. We have developed The Digital Grain Size Properties method (DGSP-method) using object oriented image analysis of very high resolution snow grain size images. Commonly used methods are based on visual interpretation, which is a subjective method providing only mean grain size does not retrieve size distribution within each sample.

    This is a first attempt to validate satellite information by the in situ measurements from JASE (Japanese Swedish Antarctic Expedition) 2007/2008 using digital image processing. The DSGP-method is based on in-field photography of snow and pixel-based object oriented image analysis. The results show shows decreasing grain size towards the centre of Antarctica and larger grains in the coastal areas. The data used to validate is three different products based on two different types of optic satellite sensors; MERIS (Medium Resolution Imaging Spectrometer) and MODIS (Moderate Resolution Imaging Spectroradiometer).  A first validation captures a cluster relation between grain size in the coastal and at the plateau and optical satellite reflection.

  • 8.
    Ingvander, Susanne
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Dahlke, Helen E.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Jansson, Peter
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Surdyk, Sylviane
    In situ sampled snow particle sizes of the East Antarctic ice sheet and their relation to physical and remotely sensed snow surface parameters2013In: Annals of Glaciology, ISSN 0260-3055, E-ISSN 1727-5644, Vol. 54, no 62, p. 166-174Article in journal (Refereed)
    Abstract [en]

    Knowledge of snow properties across Antarctica is important in estimating how climate could potentially influence the mass balance of the Antarctic ice sheet. However, measuring these variables has proven to be challenging because appropriate techniques have not yet been developed and extensive datasets of field estimates are lacking. The goal of this study was to estimate the relationship between field-observed snow particle-size parameters from across the East Antarctic ice sheet and a suite of spatial datasets (i.e. topography, remote-sensing data) using a principal component analysis (PCA). Five snow particle-size parameters were correlated to spatial datasets of the following five groups: (1) relief properties such as elevation and slope; (2) remote-sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) and synthetic aperture radar (SAR) sensors; (3) spatially interpolated data (i.e. 10 m maps of temperature and approximate snow accumulation in kg m(-2) a(-1)); (4) field-retrieved data on surface roughness; and (5) in situ elevation and distance from the coast. The results show that the relief parameter slope correlated best with the snow particle length and area (r=0.76, r=0.80). Further, the PCA indicated that the different remote-sensing parameters correlated differently with the size parameters and that the most common parameter in visual analysis, particle length (grain diameter), is not always the optimal parameter to characterize the snow particle size as, for example, area correlates better to slope and aspect than length.

  • 9.
    Ingvander, Susanne
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Jansson, Peter
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Brown, Ian A.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Fujita, Shuji
    Sugyama, Shin
    Surdyk, Sylviane
    Enomoto, Hiroyouki
    Holmlund, Per
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
    Regional and local Snow Grain Size variations in Dronning Maud Land, Antarctica and analysis of various distribution scalesManuscript (preprint) (Other academic)
    Abstract [en]

    Understanding spatial snow particle size variations are key to help interpretation of remotely sensed data of snow cover. In the case of Antarctica, remote sensing is the only viable option to estimate the surface mass balance of the ice sheet on continental scale. We have investigated snow particle size variability along a transect from the coast onto the polar plateau in Dronning Maud Land, Antarctica, to better understand the spatial and temporal variations in surface snow properties. Two daily samples were collected during a 55 day traverse to capture the regional variability. Local variability was assessed by sampling in grids at selected locations and the particle size and shape distributions for each site was analysed through digital image analysis, which has the benefit of yielding large quantities of reproducible quantitative data without the need for advanced laboratory analysis. The results provide an understanding of the complexity of snow particle size variability at different scales and show a variability range from 0.18–3.31 mm depending on the sample type (surface, grid or pit). We can verify relationships between grain size and both elevation and distance to the coast (moisture source) but have also identified regional seasonal changes, particularly on the lower elevations of the polar plateau. Our data provide possibilities to quantitatively assess the optical properties of the surface snow for remote sensing. The details of the spatial and temporal variations observed in our data provides a basis for further studies of the complex and coupled processes affecting snow particle size and the interpretation of remote sensing of snow covered areas.

  • 10.
    Ingvander, Susanne
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Jansson, Peter
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Brown, Ian A.
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Fujita, Shuji
    Sugyama, Shin
    Surdyk, Sylviane
    Enomoto, Hiroyuki
    Hansson, Margareta
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Holmlund, Per
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Snow particle sizes and their distributions in Dronning Maud Land, Antarctica, at sample, local and regional scales2016In: Antarctic Science, ISSN 0954-1020, E-ISSN 1365-2079, Vol. 28, no 3, p. 219-231Article in journal (Refereed)
    Abstract [en]

    In this study, snow particle size variability was investigated along a transect in Dronning Maud Land from the coast to the polar plateau. The aim of the study was to better understand the spatial and temporal variations in surface snow properties. Samples were collected twice daily during a traverse in 2007-08 to capture regional variability. Local variability was assessed by sampling in 10 x 10m grids (5m spacing) at selected locations. The particle size and shape distributions for each site were analysed through digital image analysis. Snow particle size variability is complex at different scales, and shows an internal variability of 0.18-3.31 mm depending on the sample type (surface, grid or pit). Relationships were verified between particle size and both elevation and distance to the coast (moisture source). Regional seasonal changes were also identified, particularly on the lower elevations of the polar plateau. This dataset may be used to quantitatively analyse the optical properties of surface snow for remote sensing. The details of the spatial and temporal variations observed in our data provide a basis for further studies of the complex and coupled processes affecting snow particle size and the interpretation of remote sensing of snow covered areas.

  • 11.
    Ingvander, Susanne
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Johansson, Cecilia
    Jansson, Peter
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Pettersson, Rickard
    Comparison between digital and manual methods of snow grain size determination2012In: Hydrology Research, ISSN 0029-1277, Vol. 43, no 3, p. 192-202Article in journal (Refereed)
    Abstract [en]

    Maintaining long time series of observations of the Cryosphere is a key issue in climate research. Long observational time series involve problems due to change in methodology or observers. In order to extend time series and introduce new methods, careful comparisons must be made to ensure homogeneity in the observational data. We have compared an established method for snow grain-size observations used by the Abisko Scientific Research Station (ASRS) in northern Sweden, based on visual interpretation, with a newly developed method for Digital Snow Particle Properties (DSPP) analysis. Transition from subjective visual method into digital reproducible analysis creates less subjective and more comparable results. The ASRS method generates size classifications excluding quantitative analysis size ranges. By determining the sizes of the classified snow using the DSPP-method, actual size ranges for classified snow can be established. By performing a digital analysis of the reference samples and the snow samples classified, we can compare the ASRS classification system to existing official classification systems. The results indicate underestimation of the visual particle size in comparison to the reference samples. Our results show how to quantify the historical data set, which enables us to perform quantitative analysis on the historical data set.

  • 12.
    Ingvander, Susanne
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Rosqvist, Gunhild
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Svensson, Jonas
    Dahlke, Helen E.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Seasonal and interannual variability of elemental carbon in the snowpack of Storglaciaren, northern Sweden2013In: Annals of Glaciology, ISSN 0260-3055, E-ISSN 1727-5644, Vol. 54, no 62, p. 50-58Article in journal (Refereed)
    Abstract [en]

    We studied the variability of elemental carbon (EC) over 3 years (2009-11) in the winter snowpack of Storglaciaren, Sweden. The goal of this study was to relate the seasonal variation in EC to specific snow accumulation events in order to improve understanding of how different atmospheric circulation patterns control the deposition of EC. Specifically, we related meteorological parameters (e.g. wind direction, precipitation) to snow physical properties, EC content, stable-isotope 8180 ratios and anion concentrations in the snowpack. The distribution of EC in the snowpack varied between years. Low EC contents corresponded to a predominance of weather systems originating in the northwest, i.e. North Atlantic. Analysis of single layers within the snowpacks showed that snow layers enriched in heavy isotopes coincided predominantly with low EC contents but high chloride and sulfate concentration. Based on this isotopic and geochemical evidence, snow deposited during these events had a strong oceanic, i.e. North Atlantic, imprint. In contrast, snow layers with high EC content coincided with snow layers depleted in heavy isotopes but high anion concentrations, indicating a more continental source of air masses and origin of EC from industrial emissions.

  • 13. Johansson, Cecilia
    et al.
    Ingvander, Susanne
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    A model for the snow water equivalent derived from stratigraphy observations in northern Sweden2015In: Hydrology research, ISSN 1998-9563, Vol. 46, no 6, p. 984-995Article in journal (Refereed)
    Abstract [en]

    A new parameterization of snow water equivalent (SWE) based on snow depth (HS) has been developed from observations made in northern Sweden. When applying previous SWE parameterization from the Alps on observations from northern Sweden, the SWE values are systematically 20% lower. The new SWE parameterization is derived from a snow layer density regression model using snow layer hardness and snow particle size. The model was evaluated with a detailed field reference dataset, and then applied to the long-term Abisko Scientific Research Station stratigraphic snowpack dataset The model enables a regional adjustment of snow layer density values for northern Swedish conditions. The snow layer density model provides an accurate estimation of snow bulk density used to derive the SWE parameterization based solely on HS. Snow depth observations are made on a daily basis; by applying our new parameterization, daily values of SWE can be obtained for northern Scandinavian conditions, which can be used, for example, for hydropower production planning and risk assessments.

  • 14. Rasmus, Sirpa
    et al.
    Boelhouwers, Jan
    Briede, Agrita
    Brown, Ian A.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Falarz, Malgorzata
    Ingvander, Susanne
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Jaagus, Jaak
    Kitaev, Lev
    Mercer, Andrew
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Rimkus, Egidijus
    Recent Change - Terrestrial Cryosphere2015In: Second Assessment of Climate Change for the Baltic Sea Basin / [ed] The BACC II Author Team, Springer, 2015, p. 117-129Chapter in book (Refereed)
    Abstract [en]

    This chapter compiles and assesses information on recent and current change within the terrestrial cryosphere of the Baltic Sea drainage basin. Findings are based on long-term observations. Snow cover extent (SCE), duration and amount have shown a widespread decrease although there is large interannual and regional variation. Few data are available on changes in snow structural properties. There is no evidence for a recent change in the frequency or severity of snow-related extreme events. There has been a decrease in glacier coverage in Sweden and glacier ice thickness in inland Scandinavia. The European permafrost is warming, and there has been a northward retreat of the southern boundary of near-surface permafrost in European Russia.

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