• Canada
• Research dataclose
• 2013-2022close
• Dataset close
• English close
• European Marine Science close
• NEANIAS Atmospheric Research Community close
• Rural Digital Europe close

Narrowing uncertainties about carbon cycling is important in the Arctic where rapid environmental changes contribute to enhanced mobilization of carbon. Here we quantify soil organic carbon (SOC) contents of permafrost soils along the Yukon Coastal Plain and determine the annual fluxes from erosion. Different terrain units are assessed based on surficial geology, morphology, and ground ice conditions. To account for the volume of wedge ice and massive ice in a unit, sample SOC contents are reduced by 19% and sediment contents by 16%. The SOC content in a 1 m**2 column of soil varies according to the height of the bluff, ranging from 30 to 662 kg, with a mean value of 183 kg. Forty-four per cent of the SOC is within the top 1 m of soil and values vary based on surficial materials, ranging from 30 to 53 kg C/m**3, with a mean of 41 kg. Eighty per cent of the shoreline is erosive with a mean annual rate of change is 0.7 m/a. This results in a SOC flux per meter of shoreline of 131 kg C/m/a, and a total flux for the entire Yukon coast of 35.5 10**6 kg C/a (0.036 Tg C/a). The mean flux of sediment per meter of shoreline is 5.3 10**3 kg/m/a, with a total flux of 1,832.0 10**6 kg/a (1.832 Tg/a). Sedimentation rates indicate that approximately 13% of the eroded carbon is sequestered in nearshore sediments, where the overwhelming majority of organic carbon is of terrestrial origin. Supplement to: Couture, Nicole; Irrgang, Anna Maria; Pollard, Wayne H; Lantuit, Hugues; Fritz, Michael (2018): Coastal Erosion of Permafrost Soils Along the Yukon Coastal Plain and Fluxes of Organic Carbon to the Canadian Beaufort Sea. Journal of Geophysical Research: Biogeosciences

Rapid changes in ocean circulation and climate have been observed in marine-sediment and ice cores over the last glacial period and deglaciation, highlighting the non-linear character of the climate system and underlining the possibility of rapid climate shifts in response to anthropogenic greenhouse gas forcing. To date, these rapid changes in climate and ocean circulation are still not fully explained. One obstacle hindering progress in our understanding of the interactions between past ocean circulation and climate changes is the difficulty of accurately dating marine cores. Here, we present a set of 92 marine sediment cores from the Atlantic Ocean for which we have established age-depth models that are consistent with the Greenland GICC05 ice core chronology, and computed the associated dating uncertainties, using a new deposition modeling technique. This is the first set of consistently dated marine sediment cores enabling paleoclimate scientists to evaluate leads/lags between circulation and climate changes over vast regions of the Atlantic Ocean. Moreover, this data set is of direct use in paleoclimate modeling studies.

This data collection presents snow depth, ice thickness and RADARSAT-2 (RS2) and TerraSAR-X (TSX) backscattering coefficient data over selected homogenous first-year smooth and undeformed sea ice areas in Salluit, Deception Bay and Kangiqsujuaq. These study sites are fjords of the Hudson Strait located in Nunavik. The data covers three winter seasons: 2016, 2017, 2018. Snow depth and ice thicknesses were measured at roughly 30 locations covering each site, twice per season (in January-February and April-May), by direct measurements. High-resolution SAR images were acquired from December to May every 24 days for RS2 and every 11 days for TSX. Data is presented for HH, HV, VV, co-polarization ratios and Cloude-Pottier decomposition parameters for the Wide-Fine Quad-Pol RS2 data, and for the HH backscattering coefficient for the Stripmap TSX data. These acquisitions were made to explore the relationship between snow depth, ice thickness and SAR. For the "SAR parameter statistics at AOI for RADARSAT-2 and TerraSAR-X" dataset, pixel median value was computed over areas of interest (AOIs) roughly 120 by 100 m and each containing between 600 and 650 pixels were distributed over the homogeneous study areas in a grid-like pattern with 0.7 to 1 km separation, avoiding special features like the shore or a ship's track. The Salluit, Deception Bay, and Kangiqsujuaq study areas counted 35, 43, and 78 AOIs, respectively. For the "SAR parameter values at measurement locations" files, pixel values were averaged over a 3x3 window for RS2 (576 m2) and a 9x9 window for TSX (506 m2). This data collection contains supplementary materials to Dufour-Beauséjour et al (2021).

Light emerging from natural water bodies and measured by remote sensing radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities to characterize aquatic ecosystems. Such upcoming missions include NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission; the NASA Surface Biology and Geology observable mission; and NASA Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) - Next Generation airborne missions. In anticipation of these missions, we present an organized dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP/AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland waters, estuaries and oceans. Specific in situ measurements include coefficients describing particulate absorption, particulate attenuation, non-algal particulate absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, and total backscattering, as well as remote sensing reflectance, and irradiance reflectance. Supplement to: Casey, Kimberly A; Rousseaux, Cecile S; Gregg, Watson W; Boss, Emmanuel; Chase, Alison P; Craig, Susanne E; Mouw, Colleen B; Reynolds, Rick A; Stramski, Dariusz; Ackleson, Steven G; Bricaud, Annick; Schaeffer, Blake; Lewis, Marlon R; Maritorena, Stéphane (2020): A global compilation of in situ aquatic high spectral resolution inherent and apparent optical property data for remote sensing applications. Earth System Science Data, 12(2), 1123-1139 Version comment:2020-05-20: Version 2 (xlsx files updated)

This dataset is part of the ESA Data User Element (DUE) Permafrost Full Product Set (doi:10.1594/PANGAEA.780111).The Land Surface Temperature (LST) products and services identified by users for the pan-Arctic (25 km resolution) scales include weekly and monthly averages from 2000 to 2010 from which annual averages can also be calculated. The LST processing integrates the LST level 2 products from MODIS and AATSR distributed by NASA and ESA, respectively. Post-processing functions supply University Waterloo-level-3 weekly and monthly LST products for regional (1 km) and pan-Arctic (25 km) scales. Thepan-Arctic product, with a spatial resolution of 25 km, is produced by spatial averaging of 1-km observations. MOD11_L2 and MYD11_L2 LST (Version 5 from NASA Terra and Aqua satellites) and ATS_NR_2P (from ESA Envisat satellite) products at 1 km resolution are used as input data to generate pan-Arctic and regional products. The original geo-located LST observations are characterized by an irregular distribution based on the satellite orbits. The Northern Hemisphere EASE-Grid Lambert Equal Area Azimuthal projection with a sphere datum (with a radius of 6371.228 km) was selected as the standard projection for the operational pan-Arctic and regional products. Original MODIS and AATSR LST level 2 observations are projected using the EASE-Grid coordinate system and interpolated to a regular EASE-Grid with 1 km spacing using triangulation. The EASE-Grid projection was chosen since this is the system adopted by the GlobSnow project and for most snow and ice products distributed by NSIDC. Local time is calculated using UTC acquisition time and longitude. UTC is extracted from ADS information for AATSR data and from the file name of MODIS level 2 (Terra and Aqua) products, yielding a temporal accuracy of ± 15 minutes, which is found to be sufficient for weekly and monthly products. Temporal aggregation is applied to both 1 km and 25 km data to produce weekly and monthly LST averages. Interpolated LST observations on a 1 km grid (regional product) and 25 km (pan-Arctic product) are aggregated into two bins; a day-time bin (from 6 a.m. to 6 p.m. local time) and a night-time bin (6 p.m. to 6 a.m. of the next day) within the aggregation period (week or month). The definition of day and night does not take in account the notion of polar darkness and does not consider the seasonal changes of day length. It was defined to force final products to have an equal number of observations around the day. A mid range average is calculated by taking the day-time and night-time average to avoid daily diurnal fluctuations during the week or month of interest. Known issues: the LST data are all measured during clear-sky conditions. The influence of clouds on surface temperature (e.g. temperature warmer under clouds in winter) is not reflected in the LSTs. This makes the LST colder than in reality due to the isolative effect of clouds. Each LST file contains 6 bands: the datafiles 001 to 006, bands 001, 003, 005 are the LST averages and bands 002, 004, 006 are supplementary quality information: Bands with averages of LST: 001 - Weekly or monthly aggregated average LST product based on equal weight of average day-time (003) and night-time (005) LST values. 003 - Average day-time weekly or monthly LST based on all cloud free observations falling during 6 a.m. to 6 p.m. local time. 005 - Average night-time weekly or monthly LST based on all cloud free observations falling into each pixel cell during 6 p.m. to 6 a.m. local time. Supplementary information bands: 002 - Number of LST cloud free observations falling into each pixel for the aggregation (weekly or monthly) period. Associated with LST file 001. 004 - Number of LST cloud free observations during day-time (6 a.m. to 6 p.m. local time) falling into each pixel. Associated with LST file 003. 006 - Number of LST cloud free observations during night-time (6 p.m. to 6 a.m. local time) falling into each pixel. Associated with LST file 005.

If not reported, water-filled pore space (WFPS) was calculated as follows: WFPS (%) = VWC/(1-BD/PD)*100, where VWC is the volumetric water content, BD is the bulk density, and PD is the particle density. If BD was not reported, BD was estimated from the SOM content using functions developed for Arctic soils (Hossain et al, 2015) as follows: 0.075+1.301*EXP(-0.06*SOM) for mineral soils, and 0.043*0+4.258*EXP(-0.047*SOM) for organic soils. If not reported, SOM via loss on ignition was derived from the soil carbon content as follows: SOM (%) = C content*2. If the resulting SOM value was >100%, SOM (%) = C content*1.724. PD can be derived as follows (Okruszko, 1971): PD = 0.011*(100-SOM)+1.451 (see references Okruszko, 1971 and Hossain et al, 2015). The term ”topsoil” depends on the exact depths the soil characteristics are reported in the individual publications, but generally the soil layer of 0–10cm was used. Column ”SOC” includes mostly total soil C content (reported in the majority of studies), and on some occasions total organic C. This dataset is a synthesis of published nitrous oxide (N2O) fluxes from permafrost-affected soils in Arctic, Antarctic, and Alpine permafrost regions. The data includes mean N2O flux rates measured under field (in situ) conditions and in intact plant-soil systems (mesocosms) under near-field conditions. The dataset further includes explanatory environmental parameters such as meteorological data, soil physical-chemical properties, as well as site and experimental information. Data has been synthesized from published studies (see 'Further details'), and in some cases the authors of published studies have been contacted for additional site-level information. The dataset includes studies published until 2019. We encourage linking additional N2O flux data from unpublished and future studies with similar metadata structure to this dataset, to produce a comprehensive, findable database for N2O fluxes from permafrost regions.