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- Killie, Mari Anne, et al.
(författare)
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Svalbard snow and sea-ice cover : comparing satellite data, on-site measurements, and modelling results (SvalSCESIA)
- 2021
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Ingår i: SESS report 2020. - Longyearbyen : Svalbard Integrated Arctic Earth Observing System. - 9788269152883 - 9788269152890 ; , s. 220-235
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Bokkapitel (refereegranskat)abstract
- Fundamental knowledge gaps and scaling issues hamper efforts to determine how changes in snow cover and snow distribution affect ecosystems. The presence of snow cover has huge impact on Arctic ecosystems, human activities, atmospheric processes and Earth’s surface energy balance. Mapping snow cover over large regions is challenging because of its variability over time and space. Also, the small number of weather stations that measure snow cover contributes to a poor observational base. Svalbard is located on the border between the ice-covered Arctic Ocean and the warmer North Atlantic, which means the sea is a controlling factor for Svalbard’s climate. By using remote sensing monitoring it is possible to get a better overview of snow conditions on land. This information can be compared with on-site observations of snow, output from snow models, and evaluated in relation to the sea-ice extent in the adjacent sea. A 34-year satellite data record for snow cover indicates that snow now starts melting more than a week earlier. The total number of snow-free days in summer is increasing fastest in regions dominated by lowland valleys and coastal plains. Most noticeable are the trends centred near the large valleys of Nordenskiöld Land. Negative trends dominate the extent of the sea ice as well. There is significant and positive correlation between sea-ice area and snow-cover extent at elevations up to 250 m in June, the month when snow melt begins. Snow melt, again, is probably strongly affected by ocean–air interactions and energy exchange when warm (or cold) winds from an open (or ice-covered) ocean come in over land.
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| 2. |
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| 3. |
- Vickers, Hannah, et al.
(författare)
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A compilation of snow cover datasets for Svalbard : A multi-sensor, multi-model study
- 2021
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 13:10
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Tidskriftsartikel (refereegranskat)abstract
- Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the last several decades. However, consistent long-term monitoring of snow cover can be challenging due to differences in spatial resolution and retrieval algorithms of the different generations of satellite-based sensors. Snow models represent a complementary tool to remote sensing for snow cover monitoring, being able to fill in temporal and spatial data gaps where a lack of observations exist. This study utilized three optical remote sensing datasets and two snow models with overlapping periods of data coverage to investigate the similarities and discrepancies in snow cover estimates over Nordenskiöld Land in central Svalbard. High-resolution Sentinel-2 observations were utilized to calibrate a 20-year MODIS snow cover dataset that was subsequently used to correct snow cover fraction estimates made by the lower resolution AVHRR instrument and snow model datasets. A consistent overestimation of snow cover fraction by the lower resolution datasets was found, as well as estimates of the first snow-free day (FSFD) that were, on average, 10–15 days later when compared with the baseline MODIS estimates. Correction of the AVHRR time series produced a significantly slower decadal change in the land-averaged FSFD, indicating that caution should be exercised when interpreting climate-related trends from earlier lower resolution observations. Substantial differences in the dynamic characteristics of snow cover in early autumn were also present between the remote sensing and snow model datasets, which need to be investigated separately. This work demonstrates that the consistency of earlier low spatial resolution snow cover datasets can be improved by using current-day higher resolution datasets.
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