Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2013S4, deployed during SIZONet 2013

Snow height was measured by the Snow Depth Buoy 2013S4, an autonomous platform, installed on land-fast sea ice off Barrow, Alaska during SIZONet 2013. The resulting time series describes the evolution of snow height as a function of place and time between 2013-04-09 and 2013-06-28 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on land-fast sea ice. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses. Note: This data set contains only relative changes in snow height, because no initial readings of absolute snow height are available.

Downscaled land surface temperatures obtained by a particle smoothing approach, with links to NetCDF files

This dataset contains continuous time series of land surface temperature (LST) at spatial resolution of 300m around the 12 experimental sites of the PAGE21 project (grant agreement number 282700, funded by the EC seventh Framework Program theme FP7-ENV-2011). This dataset was produced from hourly LST time series at 25km scale, retrieved from SSM/I data (André et al., 2015, doi:10.1016/j.rse.2015.01.028) and downscaled to 300m using a dynamic model and a particle smoothing approach. This methodology is based on two main assumptions. First, LST spatial variability is mostly explained by land cover and soil hydric state. Second, LST is unique for a land cover class within the low resolution pixel. Given these hypotheses, this variable can be estimated using a land cover map and a physically based land surface model constrained with observations using a data assimilation process. This methodology described in Mechri et al. (2014, doi:10.1002/2013JD020354) was applied to the ORCHIDEE land surface model (Krinner et al., 2005, doi:10.1029/2003GB002199) to estimate prior values of each land cover class provided by the ESA CCI-Land Cover product (Bontemps et al., 2013) at 300m resolution . The assimilation process (particle smoother) consists in simulating ensemble of LST time series for each land cover class and for a large number of parameter sets. For each parameter set, the resulting temperatures are aggregated considering the grid fraction of each land cover and compared to the coarse observations. Miniminizing the distance between the aggregated model solutions and the observations allow us to select the simulated LST and the corresponding parameter sets which fit the observations most closely. The retained parameter sets are then duplicated and randomly perturbed before simulating the next time window. At the end, the most likely LST of each land cover class are estimated and used to reconstruct LST maps at 300m resolution using ESA CCI-Land Cover. The resulting temperature maps on which ice pixels were masked, are provided at daily time step during the nine-year analysis period (2000-2009).

Physical oceanography during SONNE cruise SO248 (BacGeoPac)

The data presented here were collected during the cruise SO248 (Project BacGeoPac) with the RV Sonne from Auckland, New Zealand to Dutch Harbor, Alaska/USA. The cruise lasted from May 1, 2016 to June 3, 2016 and 19 vertical CTD-hauls were conducted. The CTD system used during this cruise was a Sea-Bird Electronics Inc. SBE 911plus probe (SN 09-1266). The CTD was attached to a SBE 32 Carousel Water Sampler (SN 32-1119) containing 24 20-liter Ocean Test Equipment Inc. bottles. The system was equipped with double temperature (SBE 3) and conductivity sensors (SBE 4), a pressure sensor (Digiquartz) an oxygen (Aanderaa Optode 4831F) and, an altimeter (Bentos) and a chlorophyll fluorometer combined with a turbidity sensor (FluoroWetlabECO _AFL FL). The sensors were pre-calibrated by the manufacturers. The data were recorded with the Seasave V 7.23.2 software and processed using the SeaBird SBE Data Processing and the ManageCTD-software. The data were processed in the following way: Data obtained during adaptation of the CTD to ambient water conditions were removed manually. The "wildedit", "loopedit", "despike", "binavg" routines were applied. The data were also visually checked and a double sensor check was conducted. The accuracy of the double sensors derived from 56 data sets were: Temperature T = 0.0007 °C; Conductivity: C = 0,0071 mS/cm; Salinity S = 0.0081 psu. The salinity data (S by unsing pss78) were converted to absolute Salinity (SA) by using the TEOS 10 toolbox. The ship position data were derived from the shipboard GPS-system linked to the CTD data. The time zone is given in UTC. The oxygen CTD data were validated by additional measurements of 98 water samples using the Winkler titration method.

A new genus Abyssogena from deep-water vents and seeps

A new genus Abyssogena is established for A. phaseoliformis (Métivier, Okutani & Ohta, 1986) and A. kaikoi (Okutani & Métivier, 1986), which were previously assigned to the genus Calyptogena Dall, 1891, and also for two new species, A. southwardae and A. novacula. The most characteristic features of Abyssogena are an elongate shell up to about 280 mm in length; a pallial line starting from the ventral margin of the anterior adductor scar; secondary pallial attachment scars developed dorsal to the pallial line; radially arranged hinge teeth with a reduced anterior cardinal tooth in the right valve; and presence of an inner ctenidial demibranch only. Abyssogena occurs in deep water from 2,985 to 6,400 m and is distributed in the Pacific and Atlantic Oceans at cold seeps along continental margins and hydrothermal vents at mid-oceanic ridges. Some species have a remarkably wide geographic distribution; A. southwardae is present throughout the Atlantic and A. phaseoliformis is present in Japan, Kuril-Kamchatka, as well as Aleutian Trenches. No fossils of Abyssogena are known.

Dissolved organic carbon (DOC) in Arctic ground ice, from northwest Canada, east Siberia, and Alaska

Thermal permafrost degradation and coastal erosion in the Arctic remobilize substantial amounts of organic carbon (OC) and nutrients which have accumulated in late Pleistocene and Holocene unconsolidated deposits. Permafrost vulnerability to thaw subsidence, collapsing coastlines and irreversible landscape change are largely due to the presence of large amounts of massive ground ice such as ice wedges. However, ground ice has not, until now, been considered to be a source of dissolved organic carbon (DOC), dissolved inorganic carbon (DIC) and other elements which are important for ecosystems and carbon cycling. Here we show, using biogeochemical data from a large number of different ice bodies throughout the Arctic, that ice wedges have the greatest potential for DOC storage, with a maximum of 28.6 mg/L (mean: 9.6 mg/L). Variation in DOC concentration is positively correlated with and explained by the concentrations and relative amounts of typically terrestrial cations such as Mg2+ and K+. DOC sequestration into ground ice was more effective during the late Pleistocene than during the Holocene, which can be explained by rapid sediment and OC accumulation, the prevalence of more easily degradable vegetation and immediate incorporation into permafrost. We assume that pristine snowmelt is able to leach considerable amounts of well-preserved and highly bioavailable DOC as well as other elements from surface sediments, which are rapidly frozen and stored in ground ice, especially in ice wedges, even before further degradation. We found that ice wedges in the Yedoma region represent a significant DOC (45.2 Tg) and DIC (33.6 Tg) pool in permafrost areas and a freshwater reservoir of 4200 km**3. This study underlines the need to discriminate between particulate OC and DOC to assess the availability and vulnerability of the permafrost carbon pool for ecosystems and climate feedback upon mobilization.