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1. Arctic Infrastructure: Sentinel-1 and Sentinel-2 derived Arctic Coastal Human Impact dataset, Pan-Arctic Region, 2016 - 2020

   https://doi.org/10.18739/A21J97929  

   Bartsch, Annett; Pointner, Georg; Nitze, Ingmar; Cohen, Juliet; Widhalm, Barbara; Von_Baeckmann, Clemens; Tanguy, Rodrigue (January 2024, NSF Arctic Data Center)

   ### Overview The SACHI (Sentinel-1/2 derived Arctic Coastal Human Impact) dataset has been developed as part of the HORIZON2020 project Nunataryuk by b.geos (www.bgeos.com). V1 covered a 100km buffer from the Arctic Coast (land area), for areas with permafrost near the coast. V2 covers additional selected areas extending the coverage to the south. It is based on Sentinel-1 and Sentinel-2 data from 2016-2020 using the algorithms described in Bartsch et al. (2020). It is a supplement to Bartsch et al. (2023). This dataset contains detected coastal infrastructure separated into seven different categories: linear transport infrastructure (asphalt), linear transport infrastructure (gravel), linear transport infrastructure (undefined), buildings (and other constructions such as bridges), other impacted area (includes gravel pads, mining sites), airstrip, and reservoir or other water body impacted by human activities. This SACHI version 2 dataset was post-processed by the Permafrost Discovery Gateway visualization pipeline. This workflow cleaned, standardized, and visualized the data as two Tile Matrix Sets per year. One Tile Matrix Set is the data in the form of GeoPackages, or staged tiles, and the other Tile Matrix Set is the staged tiles in the form of GeoTIFF tiles. The highest resolution tiles were resampled to produce GeoTIFFs for lower resolutions. This data is visualized on the Permafrost Discovery Gateway portal: https://arcticdata.io/catalog/portals/permafrost/Imagery-Viewer ### References Bartsch, A., Widhalm, B., von Baeckmann, C., Efimova, A., Tanguy, R., and Pointner, G. (2023). Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) (v2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10160636 Bartsch, A., G. Pointner, I. Nitze, A. Efimova, D. Jakober, S. Ley, E. Högström, G. Grosse, P. Schweitzer (2021): Expanding infrastructure and growing anthropogenic impacts along Arctic coasts. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ac317 Bartsch, A., Pointner, G., Ingeman-Nielsen, T. and Lu, W. (2020), ‘Towards circumpolar mapping of Arctic settlements and infrastructure based on Sentinel-1 and Sentinel-2’, Remote Sensing 12(15), 2368. ### Access Data files output from the visualization workflow are available for download at: [http://arcticdata.io/data/10.18739/A21J97929](http://arcticdata.io/data/10.18739/A21J97929) To download all files in the command line, run the following command in a terminal: `wget -r -np -nH --cut-dirs=3 -R '\?C=' -R robots.txt https://arcticdata.io/data/10.18739/A21J97929/` To download a subdirectory of the archived files, add the subdirectories to the end of the URL above. 

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2. Arctic weather variability and connectivity

   https://doi.org/10.1038/s41467-023-42351-x  

   Meng, Jun; Fan, Jingfang; Bhatt, Uma S.; Kurths, Jürgen (October 2023, Nature Communications)

   Abstract The Arctic’s rapid sea ice decline may influence global weather patterns, making the understanding of Arctic weather variability (WV) vital for accurate weather forecasting and analyzing extreme weather events. Quantifying this WV and its impacts under human-induced climate change remains a challenge. Here we develop a complexity-based approach and discover a strong statistical correlation between intraseasonal WV in the Arctic and the Arctic Oscillation. Our findings highlight an increased variability in daily Arctic sea ice, attributed to its decline accelerated by global warming. This weather instability can influence broader regional patterns via atmospheric teleconnections, elevating risks to human activities and weather forecast predictability. Our analyses reveal these teleconnections and a positive feedback loop between Arctic and global weather instabilities, offering insights into how Arctic changes affect global weather. This framework bridges complexity science, Arctic WV, and its widespread implications. 

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3. Arctic warming and your weather: public belief in the connection: ARCTIC WARMING AND YOUR WEATHER

   https://doi.org/10.1002/joc.3796  

   Hamilton, Lawrence C.; Lemcke-Stampone, Mary (April 2014, International Journal of Climatology)
4. Sustaining Arctic Observing Networks’ (SAON) Roadmap for Arctic Observing and Data Systems (ROADS)

   https://doi.org/10.14430/arctic74330  

   Starkweather, Sandy; Larsen, Jan R.; Kruemmel, Eva; Eicken, Hajo; Arthurs, David; Bradley, Alice C.; Carlo, Nikoosh; Christensen, Tom; Daniel, Raychelle; Danielsen, Finn; et al (October 2021, ARCTIC)

   Arctic observing and data systems have been widely recognized as critical infrastructures to support decision making and understanding across sectors in the Arctic and globally. Yet due to broad and persistent issues related to coordination, deployment infrastructure and technology gaps, the Arctic remains among the most poorly observed regions on the planet from the standpoint of conventional observing systems. Sustaining Arctic Observing Networks (SAON) was initiated in 2011 to address the persistent shortcomings in the coordination of Arctic observations that are maintained by its many national and organizational partners. SAON set forth a bold vision in its 2018 – 28 strategic plan to develop a roadmap for Arctic observing and data systems (ROADS) to specifically address a key gap in coordination efforts—the current lack of a systematic planning mechanism to develop and link observing and data system requirements and implementation strategies in the Arctic region. This coordination gap has hampered partnership development and investments toward improved observing and data systems. ROADS seeks to address this shortcoming through generating a systems-level view of observing requirements and implementation strategies across SAON’s many partners through its roadmap. A critical success factor for ROADS is equitable participation of Arctic Indigenous Peoples in the design and development process, starting at the process design stage to build needed equity. ROADS is both a comprehensive concept, building from a societal benefit assessment approach, and one that can proceed step-wise so that the most imperative Arctic observations—here described as shared Arctic variables (SAVs)—can be rapidly improved. SAVs will be identified through rigorous assessment at the beginning of the ROADS process, with an emphasis in that assessment on increasing shared benefit of proposed system improvements across a range of partnerships from local to global scales. The success of the ROADS process will ultimately be measured by the realization of concrete investments in and well-structured partnerships for the improved sustainment of Arctic observing and data systems in support of societal benefit. 

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5. The Arctic

   https://doi.org/10.1175/BAMS-D-21-0086.1  

   Druckenmiller, Matthew L.; Moon, Twila A.; Thoman, Richard L.; Ballinger, Thomas J.; Berner, Logan T.; Bernhard, Germar H.; Bhatt, Uma S.; Bjerke, Jarle W.; Box, Jason E.; Brown, R.; et al (August 2021, Bulletin of the American Meteorological Society)