ALBERT

Description: Coastal erosion and flooding transform terrestrial landscapes into marine environments. In the Arctic, these processes inundate terrestrial permafrost with seawater and create submarine permafrost. Permafrost begins to warm under marine conditions, which can destabilize the sea floor and may release greenhouse gases. We report on the transition of terrestrial to submarine permafrost at a site where the timing of inundation can be inferred from the rate of coastline retreat. On Muostakh Island in the central Laptev Sea, East Siberia, changes in annual coastline position have been measured for decades and vary highly spatially. We hypothesize that these rates are inversely related to the inclination of the upper surface of submarine ice-bonded permafrost (IBP) based on the consequent duration of inundation with increasing distance from the shoreline. We compared rapidly eroding and stable coastal sections of Muostakh Island and find permafrost-table inclinations, determined using direct current resistivity, of 1 and 5 %, respectively. Determinations of submarine IBP depth from a drilling transect in the early 1980s were compared to resistivity profiles from 2011. Based on borehole observations, the thickness of unfrozen sediment overlying the IBP increased from 0 to 14 m below sea level with increasing distance from the shoreline. The geoelectrical profiles showed thickening of the unfrozen sediment overlying ice-bonded permafrost over the 28 years since drilling took place. We use geoelectrical estimates of IBP depth to estimate permafrost degradation rates since inundation. Degradation rates decreased from over 0.4 m a−1 following inundation to around 0.1 m a−1 at the latest after 60 to 110 years and remained constant at this level as the duration of inundation increased to 250 years. We suggest that long-term rates are lower than these values, as the depth to the IBP increases and thermal and porewater solute concentration gradients over depth decrease. For the study region, recent increases in coastal erosion rate and changes in benthic temperature and salinity regimes are expected to affect the depth to submarine permafrost, leading to coastal regions with shallower IBP.

Description: Ponds and lakes are abundant in Arctic permafrost lowlands. They play an important role in Arctic wetland ecosystems by regulating carbon, water, and energy fluxes and providing freshwater habitats. However, ponds, i.e. waterbodies with surface areas smaller than 1.0E+04 m2, have not been inventoried at global and regional scales. The Permafrost Region Pond and Lake Database (PeRL) presents the results of a circum-arctic effort to map ponds and lakes from modern (2002–2013) high-resolution aerial and satellite imagery with a resolution of 5 m or better that resolve waterbodies with a surface area between 1.0E+02 m2 and 1.0E+06 m2. The database also includes historical imagery from 1948 to 1965 with a resolution of 6 m or better. PeRL includes 69 maps covering a wide range of environmental conditions from tundra to boreal regions and from continuous to discontinuous permafrost zones. Waterbody maps are linked to regional permafrost landscape maps which provide information on permafrost extent, ground ice volume, geology and lithology. This paper describes waterbody classification and accuracy, and presents statistics of waterbody distribution for each site. Maps of permafrost landscapes in Alaska, Canada and Russia are used to extrapolate waterbody statistics from the site level to regional landscape units. PeRL presents pond and lake estimates for a total area of 1.4E+06 km2 across the Arctic, about 17 % of the Arctic lowland (

Description: LiDAR scanning of the Yukon Coast and Herschel Island took place during the AIRMETH (AIRborne studies of METHane emissions from Arctic wetlands) campaigns (Kohnert et al., 2014) on 10 July 2012 and on 22 July 2013. Point cloud data were acquired with a RIEGL LMSVQ580 laser scanner instrument on board the Alfred Wegener Institute's POLAR-5 science aircraft. The laser scanner was operated with a 60° scan angle at a flight height of around 200 m above ground in 2012 and 500 m in 2013. This resulted in a scan width from 200 (2012) to 500 m (2013) and a mean point-to-point distance of 0.5–1.0 m. During the flight on July 10, 2012 the weather was cloudy with a cloud base around 200 m.a.s.l. . Air temperature ranged between 10 and 12 °C with wind speed ranging from 15 to 19 km/h from easterly direction (70–90°). The last recorded storm was on June 17. During the scanning on July 22, 2013, the weather was nearly cloudless with air temperature 9 °C. Wind speed was 15 km/h from easterly direction (60–80°). The last storm before the acquisition occurred on July 2. Raw laser data were calibrated, combined with the post-processed GPS trajectory, corrected for altitude, and referenced to the EGM (Earth Gravitational Model) 2008 geoid (Pavlis et al., 2008). The final georeferenced point cloud data accuracy was determined to be better than 0.15 ± 0.1 m. The loss of accuracy varied along the flight track because of the vertical accuracy of the post-processed GPS trajectory. The GPS datawere acquired in 50Hz resolutionwith aNovatel OEM4 receiver on board POLAR-5. The GPS trajectory was post-processed using precise ephemerides and the commercial software package Waypoint 8.5 (PPP [precise point positioning] processing). For the interpolation to the final DEM an inverse distance weighting (IDW) algorithm was applied using all cloud points within a 10 m radius of each point. Finally, the DEMs from the different acquisition years were interpolated toraster grids of 1 m horizontal resolution in NAD83 UTM zone 7 coordinate system. To quantify vertical change that is significant at the 99% confidence interval, we used three times RMS error procedure by Jaw (2001). Vertical accuracies for both datasets were estimated to be 0.15 m, which results in the threshold of 0.64 m for significant vertical elevation change. The accuracy of the datasets was additionally tested at locations characterized by the presence of anthropogenic features that presumably remain stable and are not affected by vertical movements because of artificial embankments underneath them. The differences between both DEM datasets were assessed along profiles and were within the previously-stated 0.15 m uncertainty.

Description: Vast portions of Arctic and sub-Arctic Siberia, Alaska and the Yukon Territory are covered by ice-rich silty to sandy deposits that are containing large ice wedges, resulting from syngenetic sedimentation and freezing. Accompanied by wedge-ice growth in polygonal landscapes, the sedimentation process was driven by cold continental climatic and environmental conditions in unglaciated regions during the late Pleistocene, inducing the accumulation of the unique Yedoma deposits up to 〉50 meters thick. Because of fast incorporation of organic material into syngenetic permafrost during its formation, Yedoma deposits include well-preserved organic matter. Ice-rich deposits like Yedoma are especially prone to degradation triggered by climate changes or human activity. When Yedoma deposits degrade, large amounts of sequestered organic carbon as well as other nutrients are released and become part of active biogeochemical cycling. This could be of global significance for future climate warming as increased permafrost thaw is likely to lead to a positive feedback through enhanced greenhouse gas fluxes. Therefore, a detailed assessment of the current Yedoma deposit coverage and its volume is of importance to estimate its potential response to future climate changes. We synthesized the map of the coverage and thickness estimation, which will provide critical data needed for further research. In particular, this preliminary Yedoma map is a great step forward to understand the spatial heterogeneity of Yedoma deposits and its regional coverage. There will be further applications in the context of reconstructing paleo-environmental dynamics and past ecosystems like the mammoth-steppe-tundra, or ground ice distribution including future thermokarst vulnerability. Moreover, the map will be a crucial improvement of the data basis needed to refine the present-day Yedoma permafrost organic carbon inventory, which is assumed to be between 83±12 (Strauss et al., 2013, doi:10.1002/2013GL058088) and 129±30 (Walter Anthony et al., 2014, doi:10.1038/nature13560) gigatonnes (Gt) of organic carbon in perennially-frozen archives. Hence, here we synthesize data on the circum-Arctic and sub-Arctic distribution and thickness of Yedoma for compiling a preliminary circum-polar Yedoma map. For compiling this map, we used (1) maps of the previous Yedoma coverage estimates, (2) included the digitized areas from Grosse et al. (2013) as well as extracted areas of potential Yedoma distribution from additional surface geological and Quaternary geological maps (1.: 1:500,000: Q-51-V,G; P-51-A,B; P-52-A,B; Q-52-V,G; P-52-V,G; Q-51-A,B; R-51-V,G; R-52-V,G; R-52-A,B; 2.: 1:1,000,000: P-50-51; P-52-53; P-58-59; Q-42-43; Q-44-45; Q-50-51; Q-52-53; Q-54-55; Q-56-57; Q-58-59; Q-60-1; R-(40)-42; R-43-(45); R-(45)-47; R-48-(50); R-51; R-53-(55); R-(55)-57; R-58-(60); S-44-46; S-47-49; S-50-52; S-53-55; 3.: 1:2,500,000: Quaternary map of the territory of Russian Federation, 4.: Alaska Permafrost Map). The digitalization was done using GIS techniques (ArcGIS) and vectorization of raster Images (Adobe Photoshop and Illustrator). Data on Yedoma thickness are obtained from boreholes and exposures reported in the scientific literature. The map and database are still preliminary and will have to undergo a technical and scientific vetting and review process. In their current form, we included a range of attributes for Yedoma area polygons based on lithological and stratigraphical information from the original source maps as well as a confidence level for our classification of an area as Yedoma (3 stages: confirmed, likely, or uncertain). In its current version, our database includes more than 365 boreholes and exposures and more than 2000 digitized Yedoma areas. We expect that the database will continue to grow. In this preliminary stage, we estimate the Northern Hemisphere Yedoma deposit area to cover approximately 625,000 km². We estimate that 53% of the total Yedoma area today is located in the tundra zone, 47% in the taiga zone. Separated from west to east, 29% of the Yedoma area is found in North America and 71 % in North Asia. The latter include 9% in West Siberia, 11% in Central Siberia, 44% in East Siberia and 7% in Far East Russia. Adding the recent maximum Yedoma region (including all Yedoma uplands, thermokarst lakes and basins, and river valleys) of 1.4 million km² (Strauss et al., 2013, doi:10.1002/2013GL058088) and postulating that Yedoma occupied up to 80% of the adjacent formerly exposed and now flooded Beringia shelves (1.9 million km², down to 125 m below modern sea level, between 105°E - 128°W and 〉68°N), we assume that the Last Glacial Maximum Yedoma region likely covered more than 3 million km² of Beringia. Acknowledgements: This project is part of the Action Group "The Yedoma Region: A Synthesis of Circum-Arctic Distribution and Thickness" (funded by the International Permafrost Association (IPA) to J. Strauss) and is embedded into the Permafrost Carbon Network (working group Yedoma Carbon Stocks). We acknowledge the support by the European Research Council (Starting Grant #338335), the German Federal Ministry of Education and Research (Grant 01DM12011 and "CarboPerm" (03G0836A)), the Initiative and Networking Fund of the Helmholtz Association (#ERC-0013) and the German Federal Environment Agency (UBA, project UFOPLAN FKZ 3712 41 106).