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Det europeiska jordobservationsprogrammet Copernicus är ett världsledande användarstyrt program för insamling och spridning av data om jordens miljö, klimat och säkerhet (Copernicus 2017a). Insamlingen av data sker till stor del med hjälp av satelliter. Data bearbetas och tillhandahålls öppet och fritt och presenteras även i form av användaranpassade tjänster. Copernicus har idag sex tematiska tjänster som tillgodoser data- och informationsbehov inom områdena klimat, hav, land och sötvatten, atmosfär, katastrofhantering och säkerhet. Tjänsterna kan anpassas efter lokala och regionala behov. Ett viktigt användningsområde för data från Copernicus är klimatanpassning. Programmets långsiktighet är en förutsättning för att kunna följa och analysera miljöns förändring över tid på ett tillförlitligt sätt och för att kunna ta fram underlag för välgrundade beslut. För att skapa samhällsnyttor krävs att programmets data och tjänster används i bred utsträckning av många användargrupper utifrån deras behov. Denna rapport hjälper användare inom klimatanpassning att upptäcka Copernicus och ger några konkreta exempel på hur programmet kan stödja just det svenska klimatanpassningsarbetet. Sverige täcks flera gånger i veckan av satelliter som mäter tillståndet i haven, på land och i atmosfären. De europeiska rymdorganisationerna ESA och Eumetsat har EU-kommissionens uppdrag att samla in, lagra och tillhandahålla satellitdata från Copernicus till grund för bred användning, forskning och datadriven innovation. All data över svenskt territorium länkas automatiskt över från ESA till det svenska satellitverktyget Swea (Rymdstyrelsen 2017). Swea har både ett webbgränssnitt och en portal med öppet applikationsprogrammeringsgränssnitt (API). Det är möjligt att utan kostnad söka, titta på och ladda hem data från Sentinelsatelliterna (Sentinel 1– 3) och även de amerikanska Landsat-satelliterna. Data i Swea är geokorrigerade efter svenska referenssystem, höjddatamodeller och kartprojektione, The European Earth Observation initiative Copernicus is a world-leading userdriven programme for the collection and dissemination of data on the Earth's environment, climate and security (Copernicus 2017). Data collection is largely carried out using satellites. Copernicus data are processed and provided freely and openly. This data policy also includes data delivered in the form of useradapted services. Copernicus currently has six thematic services that meet data and information needs in the areas of climate, marine, land and freshwater, atmosphere, emergency management and security. The services can be adapted to local and regional needs. One important area of use of Copernicus data is climate change adaptation. Copernicus has a long-term planning horizon. This is a prerequisite for the reliable detection and assessment of environmental changes, which then can underpin informed decisions. In order to create societal benefits, the programme's data and services need to be widely used. This report helps users in the field of climate change adaptation discover Copernicus and provides some concrete examples of how the programme can support Swedish work on climate change adaptation. Sweden is covered several times a week by satellites that observe the marine, terrestrial and atmospheric environment. The European space organizations ESA and EUMETSAT have the mandate of the European Commission to collect, store and provide satellite data from Copernicus as a basis for a wide range of applications, research and data-driven innovation. All data on Swedish territory are transferred automatically from ESA to the Swedish platform Swea, run by the Swedish National Space Board (Swedish National Space Board 2017). Swea is a satellite data portal containing a web interface and an open application programming interface (API). It is possible to search for and download data from Sentinel satellites (Sentinel 1-3) and the US Landsat satellites free of charge. Data in Swea are geo-cor

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.

A new parameterization of snow water equivalent (SWE) based on snow depth (HS)has been developed from observations made in northern Sweden. When applyingprevious SWE parameterization from the Alps on observations from northern Sweden,the SWE values are systematically 20% lower. The new SWE parameterization isderived from a snow layer density regression model using snow layer hardness andsnow particle size. The model was evaluated with a detailed field reference dataset,and then applied to the long term Abisko Scientific Research Station stratigraphicsnowpack dataset. The model enables a regional adjustment of snow layer densityvalues for northern Swedish conditions. The snow layer density model provides anaccurate estimation of snow bulk density used to derive the SWE parameterizationbased solely on HS. Snow depth observations are regularly made on a daily basis; byapplying our new parameterization, daily values of SWE can be obtained for northernScandinavian conditions, which e.g. can be used for hydropower production planningand risk assessments.

Spatial variability of seasonal snow depth poses a challenge when estimating snow water equivalent (SWE) from in-situ measurements in mountainous areas. Poor accessibility, complex topographic effects and localized microclimates make extrapolation of in-situ SWE measurements to a basin scale difficult. Remotely-sensed passive microwave SWE products are also inaccurate in complex terrain and/or at the forest-alpine tundra transition zone. To address these caveats, we investigated the relative importance of landscape qualities (altitude, slope, aspect, vegetation) and climate (winter temperatures, precipitation) on SWE distribution in two sub-Arctic mountainous catchments in Hemavan, Sweden, and Wolf Creek, Yukon, Canada. The two catchments are comparable, but have contrasted climate regimes. In-situ SWE measurements were made in March-April 2014 across the forest-tundra ecotone in both catchments. These were supplemented with historical snow-survey data since 2012 in Hemavan, and 1993 in Wolf Creek. Pairwise linear regressions of SWE against different landscape factors indicate that overall, altitude exerts the largest control on SWE at both Hemavan and Wolf Creek, but its effect is lesser within individual vegetation zones. In other respects, the two sites differ. SWE is inversely correlated to surface slope at forested sites in Hemavan (R^2 = 0.57, p = 0.25), but not in Wolf Creek. Slope aspect is positively correlated with SWE at forest-tundra transition sites (R^2 = 0.49, p = 0.12) in Wolf Creek, but not in Hemavan. For alpine tundra sites, slope angle strongly influences SWE in Hemavan (R^2 = 0.58, p = 0.24), but only weakly in Wolf Creek (R^2 = 0.05, p = 0.71). We discuss possible causes of these inter-catchment differences, and also evaluate the effect of inter-annual climate variations on SWE distribution at Wolf Creek using the long-term snow-survey record. Finally, we compare and discuss SWE estimates obtained by three different field measurement methods.

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., AuthorCount:4

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., AuthorCount:4

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., Interpretation and evaluation of snow and ice from remote sensing using indigenous and scientific expertise, ISIS

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, At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Manuscript. Paper 2: Submitted. Paper 4: Manuscript. Paper 5: Accepted.

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., authorCount :26

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.

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.

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.

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.