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Poster presented during 11th International Workshop on Sea Ice Modelling, Assimilation, Observations, Predictions and Verification in Oslo, Norway (21-23.03.2023). Abstract. Three nested SWAN (Simulating Waves Nearshore) models that use global wind and wave models as boundary conditions successfully predict wind wave energy in coastal (~15 m depth) waters of central Hornsund fjord, Svalbard during ice-free days. However, when sea ice is present at the entrance to the fjord, the model set over-estimates wave height and under-estimates wave period. We present a framework that uses 1) available sea ice concentration products to improve the lowest-resolution SWAN model (Svalbard-scale) and 2) semi-automated classification of Sentinel-1A/B SAR data (2014-present) to improve medium- (Hornsund-scale) and highest- (northern bays of Hornsund) resolution SWAN models. Wave spectra and bulk parameters are compared to freely available in situ wave measurements (hourly since 2013). Accurate modelling of nearshore wave conditions will allow now-casting and scenario modelling of wave runup, flooding and coastal erosion which are critical for the safety of Polish Polar Station infrastructure.

Additional file 1. OPH Scenario Set.

An in vitro method for the time-resolved quantification of acid-mediated tooth demineralisation has been developed and evaluated against putative non-permanent protective formulations based on a series of poly(alkyl methacrylate)s. Using a thermostatted carousel, dentally relevant substrates consisting of hydroxyapatite discs or sections of bovine teeth have been exposed to aqueous citric acid under controlled conditions, before and after being treated with the polymeric coatings. The dissolution of phosphate was monitored by the determination of 31P by Inductively Coupled Plasma—Mass Spectrometry and by the spectrophotometric phosphovanadomolybdate method. Dose-response plots constructed for both groups of treated substrates have revealed that the coatings significantly reduce erosion rates but are less effective at inhibiting tooth demineralisation than the standard fluoride treatment. The approach has enabled an evaluation of the erosion-protection efficiency of each coating.

Antarctica’s Pole of Inaccessibility (Southern Pole of Inaccessibility (SPI)) is the point on the Antarctic continent farthest from its edge. Existing literature exhibits disagreement over its location. Using two revisions of the Scientific Committee on Antarctic Research’s Antarctic Digital Database, we calculate modern-day positions for the SPI around 10 years apart, based on the position of the “outer” Antarctic coastline, i.e. its boundary with the ocean. These show that the position of the SPI in the year 2010 was around 83° 54’ S, 64° 53’ E, shifting on the order of 1 km per year as a result of changes of a similar magnitude in the Amery, Ronne-Filchner and Ross Ice Shelves. Excepting a position of the SPI calculated by British Antarctic Survey in 2005, to which it is very close, our newly calculated position differs by 150–900 km from others reported in the literature. We also consider the “inner” SPI, defined by the coastline with floating ice removed. The position of this SPI in 2010 is estimated as 83°37’ S, 53° 43’ E, differing significantly from other reported positions. Earlier cartographic data are probably not sufficiently accurate to allow its rate of change to be calculated meaningfully.

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

Using multispectral remote sensing, glacier surfaces can be classified into a range of zones. The properties of these classes are used for a range of glaciological applications including mass balance measurements, glacial hydrology, and melt modelling. However, it is not immediately evident that multispectral data should be optimal for imaging glaciers and ice caps. Thus, this investigation takes an inverse perspective. Taking into account spectral and radiometric properties, in situ spectral reflectance data were used to simulate glacier surface response for a suite of multispectral sensors. Sensor-simulated data were classified and compared. In addition, airborne multispectral imagery was classified for a range of spatial resolutions and intercompared in three different ways. In these analyses, the most important property which determined the suitability of a multispectral imager for glacier surface classification was its radiometric range (i.e. gain settings). Low resolution imagery (250. m. pixels) is too coarse to represent the true complexity present on a glacier while medium resolution imagery (60. m, 30. m, or 20. m) accurately represented the results derived from high resolution airborne imagery. Of those studied here, the satellite imagers currently in use that are most suitable for glacier surface classification are Landsat TM/ETM. + and ASTER (each with particular gain settings). Both Sentinel-2 and the OLI on Landsat 8 are also expected to be similarly qualified. Landsat MSS is also found to be radiometrically well-suited for glacier surface classification, but its lower spatial resolution makes it a secondary selection. © 2013 Elsevier Inc., A. Pope was supported by the National Science Foundation Graduate Research Fellowship Programme under Grant No. DGE-1038596. Further research support came from UK Natural Environment Research Council's Field Spectroscopy Facility, ARCFAC (the European Centre for Arctic Environmental Research), Trinity College Cambridge, Sigma Xi, the Norwegian Marshall Fund, the Explorers Club, the National Geographic Society Young Explorers Program, the Scott Polar Research Institute, the Cambridge University Geography Department, the Cambridge University Department of Anglo-Saxon, Norse, and Celtic Studies, and the Cambridge University Worts Fund.

© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. The transition zone between the boreal forest and Arctic tundra, the forest-tundra ecotone (FTE), is an area of high ecological and climatological significance. Despite its importance, a globally consistent high spatial resolution mapping is lacking. Accurate mapping of the FTE requires the use of satellite remote sensing data. Here we use the Landsat Vegetation Continuous Fields (VCF) product and reference point data to derive the location and characteristics of the FTE. An image texture-based supervised classification scheme is developed based on a study area in Central Eurasia to statistically exploit the spatial patterns of the transition zone. Texture statistics for the VCF image are derived from the grey-level co-occurrence matrix (GLCM) based on which the study area is classified into forest, tundra, and FTEs. Adaptive parameterization is implemented to achieve optimal classification performance in the study area. This method is further applied to six additional study areas around the circumarctic region to test its adaptability. In all study areas, this method achieves better FTE delineation results than previously reported methods, showing better classification accuracies (average of 0.826) and more realistic and complete representation of the FTE as shown by visual examination. This shows the universal applicability of the method and it is potential to be used to achieve more detailed and accurate circumarctic mapping of the FTE, which could serve as the basis of time series analysis of FTE positions, eventually contributing to a better understanding of the inter-relations between climate change and shifts in sub-arctic vegetation.

The identification and quantification of Antarctic Pygoscelis penguin colonies depends increasingly on recognition of the characteristic optical properties of guano deposits, but almost all knowledge of these properties until now has been compromised by resolution and atmospheric propagation effects. Here we present hyperspectral reflectance data in the range 350–2500 nm, collected in situ from fresh guano deposits in Pygoscelis penguin colonies on Signy Island, South Orkney Islands. The period of data collection included the transition from predominantly white guano to the pink coloration characteristic of a krill-rich diet. The main identifiable features in the spectra are a broad absorption feature centred around 550 nm, responsible for the pink coloration and identified with the pigment astaxanthin, as well as several water absorption features. Variations in these features are responsible for differentiation between spectra. From these results we propose two spectral indices suitable for use with satellite data, one of which responds to the presence of astaxanthin in the guano and the other to water. Our results do not allow us to differentiate between penguin species from their guano, but do suggest that the breeding phenology of Pygoscelis penguins could be determined from a time series of multispectral imagery.

This study proposes a new technique involving texture-based image classification to categorize altitudinal FTEs by the degree of fragmentation of the interface. This allows a) universally adaptable altitudinal FTE categorization based on widely available satellite data products and b) assessment of sensitivity of altitudinal FTEs to shift with climate change at different locations based on the spatial distribution of the corresponding categories. The FTE categorization scheme used in this study corresponds partly to the globally occurring primary altitudinal FTE ‘forms.’ Specifically, ‘diffuse’ and ‘abrupt’ FTEs are recognized and separated. Normalized Difference Vegetation Index (NDVI) calculated from Sentinel-2 imagery is used for FTE delineation and categorization. A technique named FOurier-based Textural Ordination (FOTO) is implemented to extract textural information based on NDVI variations in image windows, and supervised classification is used to further separate these windows into FTE categories based on texture. The analysis is conducted on part of the Khibiny Mountains, Kola Peninsula, Russia, and further tested on six other study areas spread across the circumarctic region. The proposed method is able to adapt to different study areas with minimum changes in parameterization, and effectively extract altitudinal FTEs and categorize them into different FTE forms with satisfactory accuracies.

Arctic Fennoscandia has undergone significant climate change over recent decades. Reanalysis datasets allow us to understand the atmospheric processes driving such changes. Here we evaluate four reanalyses against observations of near-surface air temperature (SAT) and precipitation (PPN) from 35 meteorological stations across the region for the 35-year period from 1979-2013. The reanalyses compared are the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), the European Centre for Medium-Range Weather Forecast (ECMWF) Interim reanalysis (ERA-Interim), the Japanese Meteorological Agency (JMA) 55-year reanalysis (JRA-55) and National Aeronautics and Space Administration (NASA)’s Modern-Era Retrospective Analysis for Research and Applications (MERRA). All four reanalyses have an overall small cool bias across Arctic Fennoscandia, with MERRA typically ~1°C cooler than the others. They generally reproduce the broad spatial patterns of mean SAT across the region, although less well in areas of complex orography. Observations reveal a statistically significant warming across Arctic Fennoscandia, with the majority of trends significant at p < 0.01. Three reanalyses show similar regional warming but of smaller magnitude while CFSR is anomalous, even having a slight cooling in some areas. In general the other reanalyses are sufficiently accurate to correctly reproduce the varying significance of observed seasonal trends. There are much greater differences between the reanalyses when comparing PPN to observations. MERRA-Land, which merges a gauge-based dataset, is clearly the best: CFSR is the least successful, with a significant wet bias. The smoothed reanalysis orography means that the high PPN associated with the western side of the Scandinavian Mountains extends too far inland. Spatial patterns of PPN trends across the region differ markedly between the reanalyses, which have varying success at matching observations and generally fail to replicate sites with significant observed trends. Therefore, using reanalyses to analyse PPN changes in Arctic Fennoscandia should be undertaken with caution.

© 2016 Elsevier LtdValidation of scientific findings from satellite remote sensing against local ecological knowledge could make the interpretation of forest cover patterns more robust. In this paper, we examine local perceptions of forest cover change in parishes around Budongo and Bugoma for a 30-year period (1985–2014), compare the results with those obtained from remote sensing (Twongyirwe et al., 2015), and interrogate whether the perceptions could be related to livelihood typologies. First, we characterise household strategies for the entire landscape to place livelihood strategies of communities in deforestated areas in a broader local context. An in-depth questionnaire was administered to 706 households in 13 parishes situated in 4 Agro-Ecological Zones (AEZs). The data included household demographic characteristics, energy use, cropping and livestock husbandry, and seasonal time- and labour-budgets. Principal Components Analysis (PCA) and Cluster Analysis (CA) were employed to help identify dominant structures in the data. Secondly, the 375 households in 7 parishes around Budongo and Bugoma forests (part of the 706) responded to additional questions that sought their perceptions on the forest cover trend. The PCA results for the entire landscape show that significant variation amongst households is mainly related to the cultivation time input, on-farm income particularly from cropping activities, livestock husbandry, demographic characteristics, agricultural extension activities, and cultivation labour input. Hierarchical CA shows that households at the landscape level fall into about nine different types, with variation in spatial distribution. The analysis suggests that poor households do live near forested regions, and that the rural poor are more reliant on forest products than peri-urban populations. Regarding perceptions of forest cover change, the majority (70.1%: n = 375) of the respondents in the parishes think that there has been a decline in forest cover, and this percentage is larger than the percentage of non-respondents (18.9%), those that thought it had increased (5.6%), not changed (3.7%), and those that did not know (1.6%). In addition, perceptions on forest change were significantly related to the household livelihood typologies (X2 = 623.4, df = 4, p = 0.000): respondents who perceived forest cover as having declined and those that provided no response belonged to cluster 2 (“low income mixed farming households”), which is also the dominant livelihood typology around these forests. While the data largely suggest that there is a remarkable agreement between remote sensing results and local knowledge on forest change, and that local people may play a big role in filling data gaps where a dearth of information is prevalent (or where remote sensing evidence is fuzzy), there is a clear signal that people in different social classes and age groups can have very different views on what the change in forest cover might be despite what the remote sensing data show. This might have policy implications if decision makers tend to come from the groups that are not likely to have perceived forest cover change, or base their judgement on views from certain social classes. This implies that it is important to have the remote sensing data available as a counter balance to local perception (and vice versa) and therefore these data should be considered concurrently.

International scientific cooperation in the Arctic is undoubtedly the most important tool for deep and comprehensive knowledge of large-scale changes in nature and climate occurring both in the mainland part of the macroregion and in the Arctic seas. The analysis of their dynamics shows that these changes are increasing and have a growing impact on the territories of Russia, the United States (Alaska), Canada, Denmark (Greenland), Iceland, Norway and the waters of the seas and oceans that surround these countries.