Lidar-derived original point clouds, 2012 chunk01
Maximum coverage of the dataset with at least annual coverage between 2021-2023
Between 2006 and 2015 inorganic and organic hydrochemical parameters and stable water isotope characteristics were measured in lakes and ponds in continuous permafrost landscapes in the western Canadian Arctic. The dataset contains 66 individual samples from 47 distinct water bodies. Lakes and ponds were sampled in an area covering the following geographical boundaries: North: from 68.8° to 69.7° West: from -141.0° to -138.5° Water bodies are all located in the zone of continuous permafrost. The studied water bodies are located on the mainland coast of the Yukon, Canada and on Herschel Island – Qikiqtaruk off the Yukon coast. Hydrochemical data comprises: * basic parameters such as pH and electrical conductivity * major anions * major cations * dissolved organic carbon concentration * stable water isotopes
Inorganic and organic hydrochemistry of lakes and ponds in continuous permafrost landscapes in the western Canadian Arctic, 2006-2015 (CA)
Zipped shapefile of peatland distribution - North America
Carbon monoxide (CO) is an important atmospheric constituent affecting air quality, and methane (CH4) is the second most important greenhouse gas contributing to human-induced climate change. Detailed and continuous observations of these gases are necessary to better assess their impact on climate and atmospheric pollution. While surface and airborne measurements are able to accurately determine atmospheric abundances on local scales, global coverage can only be achieved using satellite instruments. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which was successfully launched in October 2017, is a spaceborne nadir-viewing imaging spectrometer measuring solar radiation reflected by the Earth in a push-broom configuration. It has a wide swath on the terrestrial surface and covers wavelength bands between the ultraviolet (UV) and the shortwave infrared (SWIR), combining a high spatial resolution with daily global coverage. These characteristics enable the determination of both gases with an unprecedented level of detail on a global scale, introducing new areas of application. Abundances of the atmospheric column-averaged dry air mole fractions XCO and XCH4 are simultaneously retrieved from TROPOMIs radiance measurements in the 2.3µm spectral range of the SWIR part of the solar spectrum using the scientific retrieval algorithm Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). This algorithm is intended to be used with the operational algorithms for mutual verification and to provide new geophysical insights. We introduce the algorithm in detail, including expected error characteristics based on synthetic data, a machine-learning-based quality filter, and a shallow learning calibration procedure applied in the post-processing of the XCH4 data. The quality of the results based on real TROPOMI data is assessed by validation with ground-based Fourier transform spectrometer (FTS) measurements providing realistic error estimates of the satellite data: the XCO data set is characterised by a random error of 5.1ppb (5.8%) and a systematic error of 1.9ppb (2.1%); the XCH4 data set exhibits a random error of 14.0ppb (0.8%) and a systematic error of 4.3ppb (0.2%). The natural XCO and XCH4 variations are well-captured by the satellite retrievals, which is demonstrated by a high correlation with the validation data (R=0.97 for XCO and R=0.91 for XCH4 based on daily averages). ###__Citation__ Schneising, O., Buchwitz, M., Reuter, M., Bovensmann, H., Burrows, J. P., Borsdorff, T., Deutscher, N. M., Feist, D. G., Griffith, D. W. T., Hase, F., Hermans, C., Iraci, L. T., Kivi, R., Landgraf, J., Morino, I., Notholt, J., Petri, C., Pollard, D. F., Roche, S., Shiomi, K., Strong, K., Sussmann, R., Velazco, V. A., Warneke, T., and Wunch, D.: A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor, Atmos. Meas. Tech., 12, 6771–6802, https://doi.org/10.5194/amt-12-6771-2019, 2019.
Global methane column-averaged dry air mole fraction (XCH4) from TROPOMI WFM-DOAS, since 2017
CSV-file created from WP4 Mackenzie 2019 particle absorption surface water __Citation:__ Matsuoka, Atsushi; Juhls, Bennet; Bécu, Guislain; Oziel, Laurent; Leymarie, Edouard; Lizotte, Martine; Ferland, Joannie; Doxaran, David; Maury, Juliette; Béguin, Marine; Laberge-Carignan, Audrey; Guilmette, Caroline; Hilborn, Andrea; Tisserand, Lucas; Devred, Emmanuel; Bossé-Demers, Thomas; Mannino, Antonio; Babin, Marcel (2021): Phytoplankton pigment concentrations measured by HPLC in the surface water of the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937585, In: Juhls, Bennet; Lizotte, Martine; Matsuoka, Atsushi; Mével, Gaëlle; Bécu, Guislain; Overduin, Pier Paul; Devred, Emmanuel; Doxaran, David; Ferland, Joannie; Forget, Marie-Hélène; Hilborn, Andrea; Leymarie, Edouard; Maury, Juliette; Oziel, Laurent; Tisserand, Lucas; Miles, Dillon; Anikina, David Obie James; Guilmette, Caroline; Béguin, Marine; Couture, Raoul-Marie; Bossé-Demers, Thomas; Laberge-Carignan, Audrey; Chaillou, Gwénaëlle; Bélanger, Simon; Bruyant, Flavienne; Babin, Marcel (2021): Hydrographical, biogeochemical and biooptical water properties in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937587
WP4 Mackenzie 2019 phytoplankton pigment concentrations HPLC surface water CSV
Zipped folder containing incubation data
ZIP file containing all datasets as tab-delimited text
Coverage of individual image scenes, used for the processing of DARTS Level 1 datasets
CARVE Atmospheric gas concentrations user guide
This dataset contains methane fluxes from peatland plateaus and thawing peatland plateaus and from burnt and unburnt forests from permafrost in subarctic Canada. Methane fluxes were monitored during summer in 2013 and 2014 in Yukon and Northwest Territories. Monitored sites included peatland plateaus and thawing features of peatland plateaus. Methane flux measurements were performed by placing on the ground PVC-made collars and chambers and monitoring headspace concentrations over time. A portable methane gas analyser Detecto Pak-Infrared (DP-IR, Heath Consultants) was used to monitor methane concentrations. A certified gas standard was used to check the performance of the analyser. The slopes of the linear regression of methane concentrations and time were used to calculate the flux rates. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Cooper, M.A.; Estop-Aragones, C.; Fisher, J.P.; Thierry, A.; Treharne, R.; Murton, J.B.; Phoenix, G.K.; Charman, D.J.; Williams, M.; Hartley, I.P. (2016). Methane fluxes from peatland plateaus and thawing peatland plateaus and from burnt and unburnt forests from permafrost in subarctic Canada. NERC Environmental Information Data Centre. (Dataset). https://doi.org/10.5285/1d4d70ad-dc38-4e5f-bc39-066babca2fb2 __You must always use the following attribution statement to acknowledge the source of the information:__ "Contains data supplied by UK Centre for Ecology & Hydrology."
Methane fluxes from burnt and unburnt forests from permafrost in subarctic Canada, 2013 & 2014
NERC EDS Environmental Information Data Centre
You must sign in to ORNL DAAC to download. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use Policy. See [ORNL DAAC Data Use and Citation Policy](https://daac.ornl.gov/about/#citation_policy) for more information.
R-55-57 Quaternary Geological Map shapefile zip
This dataset contains information obtained from the Prudhoe Bay field site in 2012. Eighteen gas flux measurements locations were established along a transect that included terrain classified according to state of ice wedge degradation. Gas flux measurement and water chemistry analyses were also performed in 16 inundated troughs, or ponds, and 3 lakes near the transect. Flux measurements of methane (CH4) and carbon dioxide (CO2) were made; a total of 201 CH4 flux measurements, and 331 CO2 flux measurements were made. Water samples were collected from soil pore waters where available, and from ponds and lakes on select field campaigns, for a total of 52 samples. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Kimberly Wickland. 2015. Gas flux and water chemistry data, Prudhoe Bay. Arctic Data Center. doi:10.18739/A29K97.
Chamber gas flux and water chemistry data, 2012, Prudhoe Bay, Alaska.
This dataset contains satellite-derived atmospheric column-average dry-air mole fractions of methane (XCH4), and is a Level 3 gridded product in Obs4MIPs format. It has been derived by the Greenhouse Gases CCI (GHG_cci) project as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme, and was obtained from an ensemble of individual Level 2 (i.e. swath) XCH4 products, retrieved from the satellite sensors SCIAMACHY / ENVISAT and TANSO-FTS / GOSAT. The versions of the Level 2 GHG-CCI data products used as input for this product are those of the GHG_cci 'Climate Research Data Package No. 3' (CRDP#3). This Level 3 Obs4MIPs XCH4 product has been specifically generated for comparisons with climate model output in the context of the CMIP5/CMIP6/IPCC experiments. ###__Citation__ When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record: Buchwitz, M.; Reuter, M.; Aben, I.; Boesch, H.; Butz, A.; Detmers, R.G.; Frankenberg, C.; Hasekamp, O.P.; Parker, R.; Schneising, O.; Somkuti, P. (2016): ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Merged SCIAMACHY and GOSAT Level 3 gridded atmospheric column-average methane (XCH4) product in Obs4MIPs format. Centre for Environmental Data Analysis, 10 October 2016. doi:10.5285/C965E4AC-D2AF-4BAA-9E99-A234E9BA0193. https://dx.doi.org/10.5285/C965E4AC-D2AF-4BAA-9E99-A234E9BA0193
Global SCIAMACHY and GOSAT Level 3 gridded atmospheric column-average methane (XCH4) product in Obs4MIPs format, 2003-2014
Methane data from Barter Island 05.09.2017
Taken from: Scarpelli, T. R., Jacob, D. J., Moran, M., Reuland, F., & Gordon, D. (2021). A gridded inventory of Canada’s anthropogenic methane emissions. Environmental Research Letters, 17(1), 014007. https://doi.org/10.1088/1748-9326/ac40b1
Normalized difference vegetation index (NDVI) trend map was computed for the Alaskan Arctic Coastal Plain between 1999 and 2014. The decadal trend of each pixel was computed using 40-110 individual Landsat (TM, ETM+, OLI) satellite images acquired during July or August. ### __Credits__ This work was supported through the Next-Generation Ecosystem Experiments (NGEE-Arctic) project supported by the Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science, and the Integrated Ecosystem Model for Alaska and Northwest Canada project supported by the USGS Alaska Climate Science Center and by the Arctic, Western, and Northwest Boreal Landscape Conservation Cooperatives Detailed information about the methods can be found in the [publication to which this dataset is a supplement](https://doi.org/10.1038/sdata.2018.58). ###__Citation__ __In order to use these data, you must cite this data set with the following citation:__ Lara, M. J. SNAP Data Portal https://doi.org/10.21429/C9F04D (2017)
NDVI trend from Landsat time series, 1999-2014, Alaskan Arctic Coastal Plain (US)
*ZIP containing feature-layer from doi.pangaea.de/10.1594/PANGAEA.861733:* iryp_data_frames iryp_field_photos iryp_field_sites QG1000k_quaternary_deposits_thickness QG1000k_yedoma_russia QG2500k_yedoma_russia SG500k_yedoma_russia SG7200k_yedoma_alaska western_yukon_loess *Description: * IRYP_yedoma_digitalization.doc *Tables:* QG1000k_genetic_description iryp_field_sites_detail
Taken from: Walz, J et al. (2018): Greenhouse gas production in degrading ice-rich permafrost deposits in northeastern Siberia. Biogeosciences, 15(17), 5423-5436, https://doi.org/10.5194/bg-15-5423-2018
This dataset contains in situ CH4 fluxes measured through the chamber technique over the growing season of 2015 (from July to September) at a wet polygon center and a dry polygon rim in Samoylov Island, Lena River Delta, Northeastern Siberia. A clipping experiment was conducted, resulting in vegetated plots with intact tundra vegetation and clipped and trenched plots where the vegetation was removed. Active layer depths are also presented.
Chamber methane fluxes with spectrometer from a polygon tundra, 2015, northeastern Siberia
Measurements of carbon dioxide (CO2) from AVOCET | Must sign in to ORNL DAAC to download
The SACHI (Sentinel-1/2 derived Arctic Coastal Human Impact) V1 dataset was 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 has been prepared as part of the ESA project EO4PAC. It 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). V1 is a supplement to Bartsch et al. (2023) and V2 to Tanguy et al. (2024). V2 consists of two shape files. 1) SACHI_v2.shp - all identified objects with infrastructure/impact classes and auxiliary information 2) SACHI_v2_granules_acquisition_dates - processed Sentinel-2 granule extent polygons with dates of all used input data Dataset reference: see Zenodo "Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) v2" (zenodo.org) __Description of fields in SACHI_v2.shp:__ class: SACHI class value. 11=linear transport infrastructure (asphalt), 12=linear transport infrastructure (gravel), 13=linear transport infrastructure (undefined), 20=buildings (and other constructions such as bridges), 30=other impacted area (includes gravel pads, mining sites), 40=airstrip, 50=reservoir or other water body impacted by human activities __Description of fields in SACHI_v2_granules_acquisition_dates.shp:__ S2_date1 to S2_date3 - dates of individual Sentinel-2 images used for averaging S1_winter - year(s) of Sentinel-1 images used for averaging (months December and/or January) ### __Notes__ Further support was received by ESA EO4PAC for v2, by ESA CCI+ Permafrost, HGF AI-CORE, and NSF Permafrost DiscoveryGateway for v1. The processing scheme was developed on a highly performant virtual machine (VM) provided by the Copernicus Research and User Support (RUS). Results are based on modified Copernicus data from 2016 to 2020. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Bartsch, A., Widhalm, B., von Baeckmann, C., Efimova, A., Tanguy, R., & 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
Arctic Coastal Human Impact dataset (SACHI) derived from Sentinel-1/2, 2016-2020, Circum-Arctic
Publication this dataset is a supplement to
CSV file created from Shevtsova, Iuliia; Heim, Birgit; Runge, Alexandra; Fuchs, Matthias; Melchert, Jan; Herzschuh, Ulrike (2024): Total above-ground biomass of 25 vegetation sites of central Lena Delta from 2018 [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.935923
21 archived (*.zip) shapefiles (*.shp) | Must sign in to ORNL DAAC to download
Land cover classification: polygons of uniform land cover types used for training the classification procedure
__Figure 8__ Remote sensing reflectance (Rrs; per steradian) spectra measured using the C-OPS between 395 and 865 nm during Legs 2, 3, and 4. Note that Rrs spectra were not measured during Leg 1. __Citation:__ Lizotte, M. Juhls, B. et al. (2023). Nunataryuk field campaigns: Understanding the origin and fate of terrestrial organic matter in the coastal waters of the Mackenzie Delta region. Earth System Science data. Earth Syst. Sci. Data, 15, 1617–1653. https://doi.org/10.5194/essd-15-1617-2023
Annual coverage of input images for 2018-2023
CSV-file created from KoPF Vegetation Mass 2018 TAB (removed Date/Time column) __Citation:__ Shevtsova, Iuliia; Heim, Birgit; Runge, Alexandra; Fuchs, Matthias; Melchert, Jan; Kruse, Stefan; Herzschuh, Ulrike (2023): Ground layer above-ground biomass of 25 sites central Lena Delta from 2018 - Raw data of dry weight for each sub-ground vegetation type sampling plot. PANGAEA, https://doi.org/10.1594/PANGAEA.956067
KoPF Vegetation Mass 2018 (Date/Time Start: 2018-04-05, Date/Time End: 2018-09-15) CSV
Arctic permafrost stores vast amounts of methane (CH4) in subsurface reservoirs. Thawing permafrost creates areas for this potent greenhouse gas to be released to the atmosphere. Identifying "hot spots" of methane flux on a local scale has been limited by the spatial scales of traditional ground-based or satellite-based methane-sampling methods. Here we present methane data from along the coast of Barter Island, Alaska that was produced with an Unmanned Aerial System and an off-the-shelf, cost-effective methane sensor. The data was recorded in Sept., 2017 as part of a larger Arctic coastal erosion investigation study by the U.S. Geological Survey. The datasets contain latitude, longitude and CH4 (ppm).
UAV-based methane data from Barter Island, September 2017, Barrow, 1986-2017, Alaska (US)
Lake change datasets for 1999-2014 and 2017-2018 based on Landsat and Sentinel-1 data as Polygon Shapefiles
Lidar-derived rasters: microtopographic imagery
Technical documentation Permafrost CCI project
Wetlands store large amounts of carbon, and depending on their status and type, they release specific amounts of methane gas to the atmosphere. The connection between wetland type and methane emission has been investigated in various studies and utilized in climate change monitoring and modelling. For improved estimation of methane emissions, land surface models require information such as the wetland fraction and its dynamics over large areas. Existing datasets of wetland dynamics present the total amount of wetland (fraction) for each model grid cell, but do not discriminate the different wetland types like permanent lakes, periodically inundated areas or peatlands. Wetland types differently influence methane fluxes and thus their contribution to the total wetland fraction should be quantified. Especially wetlands of permafrost regions are expected to have a strong impact on future climate due to soil thawing. In this study ENIVSAT ASAR Wide Swath data was tested for operational monitoring of the distribution of areas with a long-term SW near 1 (hSW) in northern Russia (SW = degree of saturation with water, 1 = saturated), which is a specific characteristic of peatlands. For the whole northern Russia, areas with hSW were delineated and discriminated from dynamic and open water bodies for the years 2007 and 2008. The area identified with this method amounts to approximately 300,000 km**2 in northern Siberia in 2007. It overlaps with zones of high carbon storage. Comparison with a range of related datasets (static and dynamic) showed that hSW represents not only peatlands but also temporary wetlands associated with post-forest fire conditions in permafrost regions. Annual long-term monitoring of change in boreal and tundra environments is possible with the presented approach. Sentinel-1, the successor of ENVISAT ASAR, will provide data that may allow continuous monitoring of these wetland dynamics in the future complementing global observations of wetland fraction. ###__Comments__ ENIVSAT ASAR Wide Swath data was tested for operational monitoring of the distribution of areas with a long-term high degree of saturation with water (hSW) as well as open water (10 day intervals). For the whole northern Russia, areas with hSW were delineated and discriminated from dynamic and open water bodies for the years 2007 and 2008. The identified areas overlap with zones of high carbon storage. Comparison with a range of related datasets (static and dynamic) showed that hSW represents not only peatlands but also temporary wetlands associated with post-forest fire conditions in permafrost regions. This experimental wetland product has been compiled within the framework of the ESA STSE ALANIS Methane project (www.alanis-methane.info). It covers most lowland areas of Siberia. The maps represent the snow-free season of 2007 and 2008, including open water with 10 day intervals for July and August 2007 over selected regions. Please consult the product guide regarding known issues and documentation. Quality is constrained by data availability, which is documented in the *day (days since last update) and *num (number of acquisitions used for the 10 day period) files. Nominal resolution is ~75m and projection is Universal Polar Stereographic North, WGS84. The the region of interest has been subdivided into 10 parts with varying extent.
Wetland maps including open water extent dynamics based on ENVISAT ASAR WS for Siberia, 2007 and 2008
This dataset provides global gridded information on lake surface area and open water CH4 emissions at a resolution of 0.25-degree x 0.25-degree for an annual climatology representative of the average conditions from 2003 to 2015. A compilation of flux data from 575 individual lake systems and 893 aggregated flux values were used, and each flux measurement was classified into one of seven ecoclimatic types. Ice-cover-regulated emission seasonality was derived from satellite microwave observations of ice cover phenology and freeze-thaw dynamics. Global lake area was determined from the merger of HydroLAKES and Climate Change Initiative Inland-Water (CCI-IW) remote-sensing data, and lakes were classified into ecoclimatic regions to facilitate linking these types with ecosystem-specific CH4 measurements in the flux compilation. Exploratory estimates of fluxes associated with ice melt and with spring and fall water-column turnover are also included. The data are provided in NetCDF format.
Global-Gridded Daily Methane Emissions from Lake Systems, 2003-2015
2013 CARVE campaign flights | Must sign in to ORNL DAAC to download
Water for nitrate (NO3), nitrite (NO2), phosphate (PO4) and silicate (SiO4) was filtered using an acid-washed 60-mL luer-lock syringe combined with an Acrodisc filter (GF/F 0.7 µm). Triplicate samples of filtrate were collected into 15-mL acid-washed polyethylene tubes. Two replicates were kept in the dark at -20°C while the third replicate was poisoned with 24 µL of mercuric chloride and subsequently stored in the dark at 4°C prior to analysis (Hansen & Koroleff, 2007; doi:10.1002/9783527613984.ch10). Nutrient concentrations were determined using an automated colorimetric procedure described in Aminot & Kérouel (2007 (see further details)).
Nutrient (NO3, NO2, PO4, SiO4) concentrations in the surface water during 4 expeditions from spring to fall in 2019, Mackenzie Delta Region (CA)
CSV-file created from WP4 Mackenzie 2019 bacteria cells __Citation:__ Lizotte, Martine; Juhls, Bennet; Bécu, Guislain; Oziel, Laurent; Leymarie, Edouard; Matsuoka, Atsushi; Ferland, Joannie; Béguin, Marine; Laberge-Carignan, Audrey; Guilmette, Caroline; Maury, Juliette; Hilborn, Andrea; Tisserand, Lucas; Devred, Emmanuel; Doxaran, David; Bossé-Demers, Thomas; Comte, Jérôme; Joux, Fabien; Babin, Marcel (2021): Bacteria cells in the surface water of the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937576, In: Juhls, Bennet; Lizotte, Martine; Matsuoka, Atsushi; Mével, Gaëlle; Bécu, Guislain; Overduin, Pier Paul; Devred, Emmanuel; Doxaran, David; Ferland, Joannie; Forget, Marie-Hélène; Hilborn, Andrea; Leymarie, Edouard; Maury, Juliette; Oziel, Laurent; Tisserand, Lucas; Miles, Dillon; Anikina, David Obie James; Guilmette, Caroline; Béguin, Marine; Couture, Raoul-Marie; Bossé-Demers, Thomas; Laberge-Carignan, Audrey; Chaillou, Gwénaëlle; Bélanger, Simon; Bruyant, Flavienne; Babin, Marcel (2021): Hydrographical, biogeochemical and biooptical water properties in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937587
Taken from: Manning, Cara C M; Zheng, Zhiyin; Fenwick, Lindsay; McCulloch, Ross D; Damm, Ellen; Izett, Robert W; Williams, William J; Zimmermann, Sarah; Vagle, Svein; Tortell, Philippe Daniel (2022): Interannual variability in methane and nitrous oxide concentrations and sea‐air fluxes across the North American Arctic Ocean (2015–2019). Global Biogeochemical Cycles, 36, e2021GB007185, https://doi.org/10.1029/2021GB007185
Land surface feature classification, Lena Delta (Russia), 2012-08-08 to 2014-07-06
Taken from: Deng, Z., Ciais, P., Tzompa-Sosa, Z. A., Saunois, M., Qiu, C., Tan, C., Sun, T., Ke, P., Cui, Y., Tanaka, K., Lin, X., Thompson, R. L., Tian, H., Yao, Y., Huang, Y., Lauerwald, R., Jain, A. K., Xu, X., Bastos, A., Sitch, S., Palmer, P. I., Lauvaux, T., d'Aspremont, A., Giron, C., Benoit, A., Poulter, B., Chang, J., Petrescu, A. M. R., Davis, S. J., Liu, Z., Grassi, G., Albergel, C., Tubiello, F. N., Perugini, L., Peters, W., and Chevallier, F.: Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions, Earth Syst. Sci. Data, 14, 1639–1675, https://doi.org/10.5194/essd-14-1639-2022, 2022.
geopackage containing information for the 520 extracted thermokarst lagoons, including coordinates, lagoon area, and connectivity classification
Zipped files containing radiocarbon data
Lidar-derived original point clouds, 2012 chunk04
Zipped shapefile of peatland distribution - Asia
Level 2 XCH4 and XCO v1.8 (swath, quality filtered) and Terms of Use
Surface Soil Moisture & Number of Measurements
The regional waterbody classification is derived from ENVISAT ASAR Wide Swath data with 150 m resolution. Maps represent the extent of water bodies for the summer months July and August for the years 2007 to 2011. Water body maps indicate the presence of water (1) and no water (0). In addition, the number of measurements used to derive the water bodies product are also provided. Water body maps are also available are for Arctic Alaska, the Mackenzie River Valley and Delta, the Laptev Sea Coast, and the Ob Estuary. This dataset is part of the ESA Data User Element (DUE) Permafrost Full Product Set (doi:10.1594/PANGAEA.780111). More information about image processing and classification can be found in the product guide.
Water Body Classification from ENVISAT ASAR Wide Swath, 2007-2008, Yakutsk, Central Yakutia (RU)
Zipped shapefile of peatland distribution - South America
This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2), methane (CH4), and water vapor concentrations, plus air temperature, pressure, relative humidity, and wind speed values over Alaska and the Yukon and Northwest Territories of Canada during 2017-07-20 to 2017-08-08. Measurements were taken onboard a DC-8 aircraft during this Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) airborne deployment over portions of the Arctic-Boreal Vulnerability Experiment (ABoVE) domain. CO2 and CH4 were measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument. Water vapor and relative humidity were measured with Diode Laser Hydrometer. Measurements of column-averaged dry-air mixing ratio CO2 measurements (XCO2) were taken with the CO2 Sounder Lidar instrument. The airborne CO2 Sounder is a pulsed, multi-wavelength Integrated Path Differential Absorption lidar. It estimates XCO2 in the nadir path from the aircraft to the scattering surface by measuring the shape of the 1572.33 nm CO2 absorption line. The data were collected in order to capture the spatial and temporal dynamics of the northern high latitude carbon cycle as part of ABoVE and are provided in ICARTT file format. There are 40 data files in ICARTT format (*.ict) included in this dataset ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Abshire, J.B., J. Mao, H. Riris, S.R. Kawa, and X. Sun. 2022. ABoVE/ASCENDS: Active Sensing of CO2, CH4, and Water Vapor, Alaska and Canada, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2050 This dataset is openly shared, without restriction, in accordance with the [EOSDIS Data Use Policy](https://earthdata.nasa.gov/earth-observation-data/data-use-policy?_ga=2.88701406.419703260.1676448349-1262327661.1663154735).
ABoVE/ASCENDS: Active Sensing of CO2, CH4, and Water Vapor, Alaska and Canada, 2017
Thermokarst lakes are thought to have been an important source of methane (CH4) during the last deglaciation when atmospheric CH4 concentrations increased rapidly. Here we demonstrate that meltwater from permafrost ice serves as an H source to CH4 production in thermokarst lakes, allowing for region-specific reconstructions of dD-CH4 emissions from Siberian and North American lakes. dD CH4 reflects regionally varying dD values of precipitation incorporated into ground ice at the time of its formation. Late Pleistocene-aged permafrost ground ice was the dominant H source to CH4 production in primary thermokarst lakes, whereas Holocene-aged permafrost ground ice contributed H to CH4 production in later generation lakes. We found that Alaskan thermokarst lake dD-CH4 was higher (-334 ± 17 per mil) than Siberian lake dD-CH4 (-381 ± 18 per mil). Weighted mean dD CH4 values for Beringian lakes ranged from -385 per mil to -382 per mil over the deglacial period. Bottom-up estimates suggest that Beringian thermokarst lakes contributed 15 ± 4 Tg CH4 /yr to the atmosphere during the Younger Dryas and 25 ± 5 Tg CH4 /yr during the Preboreal period. These estimates are supported by independent, top-down isotope mass balance calculations based on ice core dD-CH4 and d13C-CH4 records. Both approaches suggest that thermokarst lakes and boreal wetlands together were important sources of deglacial CH4.
Isotopic composition of ground ice, ebullition gases and thermokarst lake water, Alaska, 2008-2010
FLUXNET-CH4 is an initiative led by the Global Carbon Project, in close partnership with AmeriFlux and EuroFlux, to compile a global database of eddy covariance (EC) methane flux measurements. Data are standardized, post-processed (i.e., partitioned and gap-filled), and released as FLUXNET-CH4. FLUXNET-CH4 Version 1.0 includes data from 81 sites, representing freshwater, coastal, upland, natural, and managed ecosystems. The near continuous, high-frequency nature of EC measurements offers significant promise for improving our understanding of ecosystem-scale CH4 flux dynamics. The FLUXNET-CH4 Community Product is distributed in files separated by sites and by temporal aggregation resolutions (e.g., half-hourly or daily). Version information is also assigned to the file to document changes required for a site. The file naming convention below details these options for each file. Multiple files with different temporal aggregation resolution (same site, same data product) are available for download as a single ZIP file archive. Site information metadata are also provided with a data download. Data variable descriptions can be found [here](https://fluxnet.org/data/fluxnet-ch4-community-product/data-variables/). The FLUXNET-CH4 Community Product is distributed under the two tiers of the FLUXNET2015 Data Policy. Tower teams chose data policy tiers for their site. Data distributed in both tiers can be accessed from the [Data Download](https://fluxnet.org/data/fluxnet-ch4-community-product/) page for the FLUXNET-CH4 Dataset. To see a list of site-years of data available for each site, please refer to the list of sites and data availability. __IMPORTANT:__ In case of a synthesis using both CC-By-4.0 (Tier One) and Tier Two data, all data should be treated as Tier Two. See the [FLUXNET 2015 Data Policy](https://fluxnet.org/data/data-policy/) for an explanation of data tiers.
DOI specific to site | See metadata file for DOI list
Eddy covariance measurements of methane flux from freshwater wetlands, 2006-2018, Global
This dataset contains methane and nitrous oxide dissolved gas concentration, dissolved methane carbon isotope, and ancillary hydrographic data from research cruises in the North American Arctic Ocean between 2015-2018. Ocean samples for methane and nitrous oxide analysis were collected from Niskin bottles mounted on a CTD rosette. Water was collected into glass serum bottles and allowed to overflow three times before preserving with mercuric chloride and sealing with with butyl rubber stoppers and aluminum crimp seals. Gas concentrations were determined using a purge and trap system coupled to a gas chromatograph/mass spectrometer, following the method of Capelle et al. (2015). Equilibrium dry atmospheric concentrations were 328.25, 329.14, 330.11, and 330.96 ppb for N2O and 1919.64, 1933.67, 1934.92, and 1933.50 ppb for CH4 in 2015, 2016, 2017, and 2018, respectively. Equilibrium dissolved concentrations were calculated from the measured temperature and salinity following Wiesenburg and Guinasso (1979) for CH4 and Weiss and Price (1980) for N2O. Equilibrium concentrations were calculated based on sample temperature and salinity and the atmospheric N2O or CH4 concentrations measured at Barrow, Alaska by the NOAA Earth System Research Laboratory Global Monitoring Division (Dlugokencky et al., 2020a,b), with corrections to local sea level pressure and 100% humidity. Oxygen concentration was determined using an oxygen sensor mounted on the Niskin rosette, calibrated with discrete samples analyzed by Winkler titration. The mixed layer depth was defined based on a potential density difference criterion of 0.125 kg/m³ relative to the density at 5 m depth, using CTD profiles binned to 1 m. The mixed layer depth was set to 5 m as a minimum. For methane δ13C, samples were pre-concentrated through a purge and trap system (Finnigan PreCon Trace Gas Pre-Concentrator) and measured with a Finnigan Delta XP Plus mass spectrometer following the method of Damm et al. (2015).
Methane and nitrous oxide dissolved gas concentrations in seawater from the North American Arctic Ocean (2015–2018)
We mapped the occurrence of more than one million ice wedge polygons over a landscape of approximately 1,200 square kilometers, just south of Prudhoe Bay in northern Alaska, USA. The microtopographic relief at the center of each polygon was then calculated relative to the periphery, as a proxy for low-centered or high-centered form. Polygon boundaries are presented as shapefiles in UTM Zone 6N coordinates; the centroid, area, and relative elevation at the center of each instance are included in the associated attribute tables. Each shapefile covers one square kilometer of terrain; a unified attribute table in tab-delineated format is also provided. Additionally, the lidar-derived elevation data used as input for the mapping procedure are presented in GeoTIFF (rasterized) and LAZ (point-cloud) formats, and code (written in MATLAB vR2017b) has been uploaded. Additional information regarding the creation of the data sets can be found in the [accompanying manuscript in Scientific Data](https://doi.org/10.1038/s41597-020-0423-9).
Ice wedge polygon occurrence and geomorphology from LiDAR elevation data, 2012 and 2014, south of Prudhoe Bay, Alaska (US)
This dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e. "concentrations") from airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The CARVE flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas abundances. The data were derived from laboratory measurements of whole air samples collected by a Programmable Flask Package (PFP) onboard the CARVE aircraft. Air samples were collected at strategic intervals to coincide with the overflight of a ground site of interest, or when interesting geophysical conditions were encountered. While most of these samples were collected near the surface in the planetary boundary layer (PBL), on almost every flight samples were also collected in the free troposphere. A minimum of 12 flask samples were collected per flight. Whole air samples collected in the PFPs were analyzed on automated systems at the NOAA Earth System Research Laboratory (ESRL) Global Monitoring Division in Boulder, CO, which also analyzes samples from the NOAA/ESRL Global Greenhouse Gas Reference Network. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Sweeney, C., J.B. Miller, A. Karion, S.J. Dinardo, and C.E. Miller. 2016. CARVE: L2 Atmospheric Gas Concentrations, Airborne Flasks, Alaska, 2012-2015. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1404 This dataset is openly shared, without restriction, in accordance with the [EOSDIS Data Use Policy](https://earthdata.nasa.gov/earth-observation-data/data-use-policy?_ga=2.88701406.419703260.1676448349-1262327661.1663154735).
CARVE atmospheric gas concentrations, airborne flasks, Alaska, 2012-2015
__Zipped Shapefile__ Citation: Wolfe, Stephen; Morse, Peter; Parker, Ryan; Phillips, Marcus (2023), “Distribution and morphometry of pingos, western Canadian Arctic, Northwest Territories, Canada: Datasets and Supplementary Materials”, Mendeley Data, V1, doi: 10.17632/kgbsjvrj32.1
Wetland methane emission map zipped netCDF files
Land surface features (water, wetland, tundra, and bare surfaces) were classified from TerraSAR-X with a resolution of 5 m for August 2012 and July 2014. The product is based on the interpretation of polarimetric TerraSAR-X data (HH/VV polarisation). More information on the classification can be found in the product guide and the related publication.
Land Surface Feature Classification from TerraSAR-X, 2013, Lena Delta, Laptev and East Siberian Sea Region (RU)
point geopackage containing information for the 520 extracted thermokarst lagoons
Measurements of water vapor and humidity from the Diode Laser Hydrometer instrument | Must sign in to ORNL DAAC to download
Frozen state backscatter intensity was derived from Sentinel-1 acquisitions from December 2015 and 2016. By excluding the effects of liquid water content (acquisitions under frozen conditions), backscatter only depends on surface roughness and vegetation structure. C-HH winter backscatter could be shown to correspond to wetness levels. Smooth surfaces represent wetter areas than rough surfaces (with respect to wavelength, ~5.6 cm). In addition, C-band SAR information (frozen condition) can be directly translated into soil organic carbon content of the upper 0-100 cm for areas without woody vegetation. The completet dataset covers the following regions: Yamal peninsula (Russia), Usa Basin (Russia), Lena Delta (Russia), Mackenzie Delta (Canada), Barrow, Toolik and Teshekpuk Lake region (Alaska). Click the 'Groups' tab above for an overview of all datasets. More information about image processing can be found in the product guide and the related publications.
Frozen State Backscatter Intensity from Sentinel-1, 2016-2017, Mackenzie Delta, Beaufort Sea Region (CA)
Meteorological and navigation measurements from the DC-8 platform | Must sign in to ORNL DAAC to download
Layer file for visualizing final land cover maps with ArcGIS
R-55-57 Quaternary Geological Map ISO19139 xml
We present geochemical and hydrochemical information on individual permafrost and seawater samples from the Yukon Coast in the western Canadian Arctic used for an incubation experiment. The experiment mimicked erosion of permafrost coasts in the Arctic by mixing different kind of permafrost (i.e. organic-enriched and mineral) with ambient seawater at temperatures of 4 and 16°C during the course of 4 months (the approximate length of an Arctic open-water season). The data sets contain information on basic geochemical parameters measured before and after the experiment and on the production of greenhouse gases, which include carbon dioxide (CO2) and methane (CH4). This data set contains the geochemical and hydrochemical data for permafrost and seawater as well as the total amount of CO2 and CH4 cumulated during the course of the experiment. The study aims at understanding the role of eroding permafrost coasts for the Arctic carbon cycle and budget.
Geochemistry of incubated permafrost and seawater from the western Canadian Arctic
The data contains the list of pingo and pingo-like forms over Svalbard archipelago. Each pingo is assigned with following data: coordinates, absolute height of pingo baseline, height of the mound, diameter of the mound, morphology of the mound, presence or absence of craters and crater lakes, presence or absence and chemical composition of springs/icings, chemical composition of springs/icings if applicable, geomorphological positioning, positioning relative to the local Holocene transgression limit, slope exposition if applicable, observed morphology dynamics, distance to the nearest glacier, presence or absence of vegetation cover, absolute or relative age of pingo formation where available, local geological strata and tectonic province, presence of confirmed or assumed fault line. The data allows to obtain statistics necessary for analizing pingos as phenomena of Svalbard. The Iidentification of pingos is based on Svalbard aerial survey high resolution images which are publicly available at Norwegian Polar Institute online Topo Svalbard DEM resource (https://toposvalbard.npolar.no, 2008-2012). High resolution satellite images covering the whole archipelago are furthermorealso available at the Zoom Earth online database (https://zoom.earth). Pingo positioning, morphology, vegetation cover, presence of craters and distance to glaciers were analyzed using Norwegian Polar Institute Topo Svalbard S0 Terrengmodel Svalbard DEM model (2014) with 2-5 m resolution and orthophoto base map (maximum cache scaling 1:625 and resolution 0.165). Absolute height measurement accuracy is in range of ±2 m. The presence of pingo springs was determined based on earlier reported data (Liestøl, 1977) with addition of pingos where icings are clearly visible on winter/spring Topo Svalbard and Zoom Earth 2018-2020 satellite images. Special information such as absolute age, drilling results, spring composition base on published data (Orvin, 1944; Liestøl, 1977; Yoshikawa and Harada, 1995, Matsuoka et. al, 2004; Hodson et. al, 2020; Demidov et al, 2019; Demidov et. al, 2020, Demidov et. al, 2021). The positioning of pingos relative to the local Holocene transgression limit was determined by comparing pingo baseline heights with the transgression isobase map of Bondevik et al. (1995) and for Grøndalen Valley with Sharin et al. (2014). Geological data is based on bedrock geology, tectonic structure and rock type maps (Geoscience atlas Atlas of Svalbard, 2019). Personal field observations and sampling took place during summer seasons 2017-2020 on pingos of Grøndalen Valley (drilling of 4 pingos) and mounds and thermokarst funnels of Hollenderdalen Valley, in April-May 2020 on pingos and icings of Berzeliusdalen, Aurdalen, Vassdalen and Reindalen, and in March 2021 in Ebbadalen and vales around Pyramiden.
publisher catalogue and licence information
Fig. 3. Spatial distribution of the in situ surface salinity in the coastal waters for leg 1 (A), leg 2 (B), leg 3 (C) and leg 4 (D). __Citation:__ Juhls, B, Matsuoka, A., Lizotte, M. et al. (2022). Seasonal dynamics of dissolved organic matter in the Mackenzie Delta, Canadian Arctic waters: Implications for ocean colour remote sensing. Remote Sensing of Environment, 283,113327. https://doi.org/10.1016/j.rse.2022.113327.
The determination of phytoplankton using high performance liquid chromatography (HPLC) is detailed in Hooker et al. (2005; doi:10.1364/AO.44.000553) and Ras et al. (2008; doi:10.5194/bg-5-353-2008). Briefly, the pigments of particles retained on GF/F (0.7 µm) filters were extracted at -20°C with 3 mL methanol (100%). The filter samples were then disrupted using a sonicator and clarified one hour later by vacuum filtration through GF/F filters. The extracts were rapidly analyzed within 24h by HPLC with a complete Agilent Technologies system (comprising LC Chemstation software, a degasser, a binary pump, a refrigerated autosampler, a column thermostat and a diode array detector).
Phytoplankton pigment concentrations measured by HPLC in the surface water during 4 expeditions from spring to fall in 2019, Mackenzie Delta Region (CA)
Lidar-derived original point clouds, 2012 chunk06
When wet Arctic tundra soils begin to freeze in the fall, an unfrozen layer remains between the frozen surface and deeper permafrost layers. This period is known as the zero curtain, as liquid water keeps the temperature of this soil layer near 0 Celsius (C) while latent heat is gradually dissipated. This experiment compares the temperature response of the methanogenic community in the zero curtain period with that of the summer community to test whether the zero curtain methanogenic community is especially cold adapted. This dataset includes methane production rates measured in anaerobic laboratory incubations of soils collected from two dates (July and Nov 2018) at temperatures around 0, 4 and 10C. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ David Lipson. (2021). Methane production in laboratory incubations of Arctic soil at three temperatures, 2018. Arctic Data Center. doi:10.18739/A29882P2H.
Methane production in laboratory incubations of Arctic soil, Utqiaġvik, Alaska, 2018
WP4 Mackenzie 2019 temperature & salinity surface water
2014 Tower data | Must sign in to ORNL DAAC to download
Particulate organic carbon (POC) and particulate organic nitrogen (PON) concentrations were obtained from water samples filtered on precombusted (450°C for >5 hours) 47mm Whatmann GF/F (0.7 µm) filters. The filters were dried overnight at 60°C and vacuum-sealed for storage in aluminum foil kept at -20°C until analysis. To determine POC and PON concentrations, the filters were acidified with 200-350 µl HCl 2N to remove carbonates, dried at 60°C overnight and then burned on a pre-calibrated CHN analyzer (Perkin Elmer, combustion at 925°C) for determination of the CO2 produced (Doxaran et al. 2012; doi:10.5194/bg-9-3213-2012).
Particulate organic carbon (POC) and particulate organic nitrogen (PON) concentrations in the surface water during 4 expeditions from spring to fall in 2019, Mackenzie Delta Region (CA)
Footprints of RTS and ALD features, maximum extent aggregated per calendar year
created from https://ars.els-cdn.com/content/image/1-s2.0-S0277379123001038-mmc1.xlsx __Citation:__ Richard E. Fewster, Paul J. Morris, Graeme T. Swindles, Ruza F. Ivanovic, Claire C. Treat, Miriam C. Jones, Holocene vegetation dynamics of circum-Arctic permafrost peatlands, Quaternary Science Reviews, Volume 307, 2023, 108055, ISSN 0277-3791, https://doi.org/10.1016/j.quascirev.2023.108055
Widhalm, B.; Bartsch, A.; Goler, R. Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments. Remote Sens. 2018, 10, 551.
Publication - this dataset is a supplement to
Regional Water Bodies, 2009-2011, Ob Estuary
The Arctic Landscape EXplorer (ALEX) presents change data derived from remote sensing analysis for permafrost regions throughout the Arctic. Interactive maps provide up-to-date information on land surface changes, hotspots of disturbance, and potential areas of active permafrost thaw and erosion. Using tailored visualizations and story maps as a means to more effectively communicate scientific observations of change, the tool is specifically addressing non-scientific user communities, stakeholders, and rights holders in the Arctic.
Limited subset for 2001-2015 | Must sign in to ORNL DAAC to download
This dataset contains v4.0 permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m). Case A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Westermann, S.; Barboux, C.; Bartsch, A.; Delaloye, R.; Grosse, G.; Heim, B.; Hugelius, G.; Irrgang, A.; Kääb, A.M.; Matthes, H.; Nitze, I.; Pellet, C.; Seifert, F.M.; Strozzi, T.; Wegmüller, U.; Wieczorek, M.; Wiesmann, A. (2024): ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v4.0. NERC EDS Centre for Environmental Data Analysis, 04 April 2024. doi:10.5285/20ec12f5d1f94e99aff2ed796264ee65. https://dx.doi.org/10.5285/20ec12f5d1f94e99aff2ed796264ee65
Permafrost ground temperature for the Northern Hemisphere, v4.0 from MODIS LST, ERA5, 1997-2021
CSV-file created from WP4 Mackenzie 2019 Rrs __Citation:__ Matsuoka, Atsushi; Juhls, Bennet; Bécu, Guislain; Oziel, Laurent; Leymarie, Edouard; Lizotte, Martine; Ferland, Joannie; Doxaran, David; Maury, Juliette; Babin, Marcel (2021): Remote sensing reflectance (Rrs) of the surface water of the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937583, In: Juhls, Bennet; Lizotte, Martine; Matsuoka, Atsushi; Mével, Gaëlle; Bécu, Guislain; Overduin, Pier Paul; Devred, Emmanuel; Doxaran, David; Ferland, Joannie; Forget, Marie-Hélène; Hilborn, Andrea; Leymarie, Edouard; Maury, Juliette; Oziel, Laurent; Tisserand, Lucas; Miles, Dillon; Anikina, David Obie James; Guilmette, Caroline; Béguin, Marine; Couture, Raoul-Marie; Bossé-Demers, Thomas; Laberge-Carignan, Audrey; Chaillou, Gwénaëlle; Bélanger, Simon; Bruyant, Flavienne; Babin, Marcel (2021): Hydrographical, biogeochemical and biooptical water properties in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937587
This data set provides global monthly wetland methane (CH4) emissions and uncertainty data products derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies. The data are at 0.5 by 0.5-degree resolution. Two model output data products are included in WetCHARTs v1.0: an output from the full ensemble for 2009-2010 and an output from a limited subset for 2001-2015. The intended use of the products is as a process-informed wetland CH4 emission and uncertainty data set for atmospheric chemistry and transport modelling (WetCHARTs). ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Bloom, A.A., K. Bowman, M. Lee, A.J. Turner, R. Schroeder, J.R. Worden, R.J. Weidner, K.C. McDonald, and D.J. Jacob. 2017. CMS: Global 0.5-deg Wetland Methane Emissions and Uncertainty (WetCHARTs v1.0). ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1502 This dataset is openly shared, without restriction, in accordance with the [EOSDIS Data Use Policy](https://earthdata.nasa.gov/earth-observation-data/data-use-policy?_ga=2.88701406.419703260.1676448349-1262327661.1663154735).
Global 0.5-deg Wetland Methane Emissions and Uncertainty (WetCHARTs v1.0), 2009-2010
Taken from: Walz, Josefine; Knoblauch, Christian; Böhme, Luise; Pfeiffer, Eva-Maria (2017): Regulation of soil organic matter decomposition in permafrost-affected Siberian tundra soils - Impact of oxygen availability, freezing and thawing, temperature, and labile organic matter. Soil Biology and Biochemistry, 110, 34-43, https://doi.org/10.1016/j.soilbio.2017.03.001
CSV-file created from WP4 Mackenzie 2019 bacteria cells __Citation:__ Juhls, Bennet; Bécu, Guislain; Oziel, Laurent; Leymarie, Edouard; Matsuoka, Atsushi; Lizotte, Martine; Ferland, Joannie; Overduin, Pier Paul; Béguin, Marine; Laberge-Carignan, Audrey; Guilmette, Caroline; Maury, Juliette; Hilborn, Andrea; Tisserand, Lucas; Devred, Emmanuel; Doxaran, David; Bossé-Demers, Thomas; Fritz, Michael; Babin, Marcel (2021): Dissolved organic carbon (DOC) concentration in the surface water of the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937573, In: Juhls, Bennet; Lizotte, Martine; Matsuoka, Atsushi; Mével, Gaëlle; Bécu, Guislain; Overduin, Pier Paul; Devred, Emmanuel; Doxaran, David; Ferland, Joannie; Forget, Marie-Hélène; Hilborn, Andrea; Leymarie, Edouard; Maury, Juliette; Oziel, Laurent; Tisserand, Lucas; Miles, Dillon; Anikina, David Obie James; Guilmette, Caroline; Béguin, Marine; Couture, Raoul-Marie; Bossé-Demers, Thomas; Laberge-Carignan, Audrey; Chaillou, Gwénaëlle; Bélanger, Simon; Bruyant, Flavienne; Babin, Marcel (2021): Hydrographical, biogeochemical and biooptical water properties in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937587
Regional Water Bodies, 2007-2011, Laptev Sea Region
created from https://ars.els-cdn.com/content/image/1-s2.0-S0277379123001038-mmc2.xlsx __Citation:__ Richard E. Fewster, Paul J. Morris, Graeme T. Swindles, Ruza F. Ivanovic, Claire C. Treat, Miriam C. Jones, Holocene vegetation dynamics of circum-Arctic permafrost peatlands, Quaternary Science Reviews, Volume 307, 2023, 108055, ISSN 0277-3791, https://doi.org/10.1016/j.quascirev.2023.108055
Synthesis data of plant macrofossil records CSV
photographs of Ice-Rich Yedoma permafrost sites
CSV-file created from WP4 Mackenzie 2019 CTD (removed last 7 lines) __Citation:__ Bécu, Guislain; Juhls, Bennet; Oziel, Laurent; Leymarie, Edouard; Matsuoka, Atsushi; Lizotte, Martine; Ferland, Joannie; Babin, Marcel (2021): Hydrographic (CTD) profiles in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937567, In: Juhls, Bennet; Lizotte, Martine; Matsuoka, Atsushi; Mével, Gaëlle; Bécu, Guislain; Overduin, Pier Paul; Devred, Emmanuel; Doxaran, David; Ferland, Joannie; Forget, Marie-Hélène; Hilborn, Andrea; Leymarie, Edouard; Maury, Juliette; Oziel, Laurent; Tisserand, Lucas; Miles, Dillon; Anikina, David Obie James; Guilmette, Caroline; Béguin, Marine; Couture, Raoul-Marie; Bossé-Demers, Thomas; Laberge-Carignan, Audrey; Chaillou, Gwénaëlle; Bélanger, Simon; Bruyant, Flavienne; Babin, Marcel (2021): Hydrographical, biogeochemical and biooptical water properties in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937587
This dataset contains measures of laboratory soil incubation respiration rates from permafrost in subarctic Canada. Soil cores were sampled from a peatland plateau and a thawing feature of the peatland plateau and from a unburnt and a burnt black spruce forest, and also additional sites in Yukon. Soil samples from the active layer and permafrost and from organic and mineral horizons were incubated under oxic and anoxic conditions at 5 and 15 celsius degrees. Soil samples were kept wet at water holding capacity for aerobic incubations and extra water was added in anaerobic incubations to ensure saturation and water filling of soil pores. CO2 concentrations were measured using an EGM-4 Infrared Gas Analyser (PP Systems). For anoxic conditions, incubations were flushed with N2, and CO2 and CH4 concentration were measured using a EGM4 (CO2) and a DP-IR Detecto Pak (CH4) analysers. CO2 and CH4 production rates were calculated assuming linear increase in headspace concentrations. Production rates are expressed as mg of C (either C-CO2 or C-CH4) per day and gram of dry soil. Carbon and nitrogen content are expressed as percentage of dry mass. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Estop-Aragones, C.; Fisher, J.P.; Cooper, M.A.; Thierry, A.; Treharne, R.; Murton, J.B.; Phoenix, G.K.; Charman, D.J.; Williams, M.; Hartley, I.P. (2016). Laboratory soil incubation respiration rates from permafrost in subarctic Canada. NERC Environmental Information Data Centre. (Dataset). https://doi.org/10.5285/0c79defa-d762-4ad1-a714-02e177d8e690 __You must always use the following attribution statement to acknowledge the source of the information:__ "Contains data supplied by UK Centre for Ecology & Hydrology."
Laboratory soil incubation respiration rates from permafrost in subarctic Canada, 2013 & 2014
CO2 and CH4 production in aerobic and anaerobic incubation in sediments samples from Muostakh Island (sample code MUO12), the Buor Khaya Peninsula (BK8), and Bol'shoy Lyakhovsky (L14-05 and L14-02). CO2 and CH4 production at 4°C over a 134-day (MUO12) or a 785-day incubation period (BK8, L14-05, L14-02) measured by gas chromatography.
Soil incubation data from ice-rich permafrost deposits in Northeast Siberia, 2014
This dataset contains measures of soil respired radiocarbon as CO2 and CH4 from peatland plateaus and thawing peatland plateaus and from burnt and unburnt forests from permafrost in subarctic Canada. The radiocarbon content of soil respired CO2 and CH4 was measured during summer in 2013 and 2014 in Yukon and Northwest Territories. Monitored sites included peatland plateaus, thawing features of peatland plateaus and unburnt and burnt black spruce forests. Soil respired CO2 and CH4 was collected from chambers and probes in zeolite molecular sieve cartridges (Type 13X). The cartridges were sent to the NERC Radiocarbon Facility in East Kilbride, UK for 14C analysis and were assigned a publication code. Values of radiocarbon enrichment are expressed in percent modern carbon. Ages are reported as conventional radiocarbon age with years BP (before present, where present is defined as AD 1950). ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Estop-Aragones, C.; Fisher, J.P.; Cooper, M.A.; Thierry, A.; Treharne, R.; Murton, J.B.; Phoenix, G.K.; Charman, D.J.; Williams, M.; Hartley, I.P. (2016). Laboratory soil incubation respiration rates from permafrost in subarctic Canada. NERC Environmental Information Data Centre. (Dataset). https://doi.org/10.5285/0c79defa-d762-4ad1-a714-02e177d8e690 __You must always use the following attribution statement to acknowledge the source of the information:__ "Contains data supplied by UK Centre for Ecology & Hydrology."
Soil respired radiocarbon as CO2 and CH4 from permafrost in subarctic Canada, 2013 & 2014
Regional Water Bodies, 2007-2008, Central Yakutia
This dataset contains a gridded inventory of Canada's 2018 anthropogenic methane emissions at 0.1 x 0.1 degree. National and provincial emission totals in the inventory match Canada's National Inventory Report (2020) used for reporting greenhouse gas emissions to the United Nations Framework Convention on Climate Change (UNFCCC). This dataset includes separate gridded inventory files for each emission sector and subsector as shown in Table 1 and Table 2 of Scarpelli et al. [2021]. Emissions are allocated to sources on the 0.1 x 0.1 degree grid using a collection of national and provincial geospatial datasets, and incorporates point source emissions as reported in Canada's Greenhouse Gas Reporting Program (GHGRP) in 2020. (2021-11-06) All gridded emissions files are at 0.1 x 0.1 degree resolution and reflect 2018 annual emissions, though emissions can be readily updated to other years by scaling total emissions using Canada's annually reported National Inventory Report (NIR). There is a netCDF file for each emission sector. Each netCDF contains gridded variables for (1) total sector emissions with the extension "(total)", (2) corresponding subsector and source type emissions, and (3) latitude/longitude centers and area of each grid cell. Codes used to represent sectors and subsectors follow the definitions in Canada's 2020 NIR. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Scarpelli, Tia; Jacob, Daniel; Moran, Mike; Reuland, Frances; Gordon, Deborah, 2021, "Gridded inventory of Canada's anthropogenic methane emissions for 2018", https://doi.org/10.7910/DVN/CC3KLO, Harvard Dataverse, V1
Gridded inventory of Canada's anthropogenic methane emissions for 2018
__Figure 7__ Linear regressions between particle absorption (ap(λ)) at 443 nm and (a) particulate organic carbon (POC). (b) Total particulate carbon (TPC) for the four WP4 Nunataryuk expeditions (Legs 1, 2, 3, and 4) in 2019. Equations and coefficients of determination (R2) are shown. The blue lines represent the linear regressions, whereas the shaded gray areas show the standard error around the regression lines. Note that the data are plotted on a log scale. __Citation:__ Lizotte, M. Juhls, B. et al. (2023). Nunataryuk field campaigns: Understanding the origin and fate of terrestrial organic matter in the coastal waters of the Mackenzie Delta region. Earth System Science data. Earth Syst. Sci. Data, 15, 1617–1653. https://doi.org/10.5194/essd-15-1617-2023
173 *.zip files containing 48 *.png quick-look plots | Must sign in to ORNL DAAC to download
Absorbance of particles retained on GF/F (0.7 µm) filters was measured using a Varian Cary 100 spectrophotometer equipped with an integrated sphere. Absorbance and reflectance spectra were measured by placing a sample filter in front and back of an integrating sphere, respectively (so-called Transmittance-Reflectance or T-R method; Tassan & Ferrari 1995; doi:10.4319/lo.1995.40.8.1358). An appropriate beta factor specific to the geometry of the instrument was used to calculate absorption coefficients of particles (Tassan & Ferrari 2002; doi:10.1093/plankt/24.8.757).
Particle absorption (aP) in the surface water during 4 expeditions from spring to fall in 2019, Mackenzie Delta Region (CA)
The Northern Circumpolar Soil Carbon Database version 2 (NCSCDv2) is a geospatial database created for the purpose of quantifying storage of organic carbon in soils of the northern circumpolar permafrost region down to a depth of 300 cm. The NCSCDv2 is based on polygons from different regional soils maps homogenized to the U.S. Soil Taxonomy. The NCSCDv2 contains information on fractions of coverage of different soil types (following U.S. Soil Taxonomy nomenclature) as well as estimated storage of soil organic carbon (kg/m2) between 0-30 cm, 0-100 cm, 100-200 cm and 200-300 cm depth. The database was compiled by combining and homogenizing several regional/national soil maps. To calculate storage of soil organic carbon, these soil maps have been linked to field-data on soil organic carbon storage from sites with circumpolar coverage. More information on database processing and properties can be found in the product guide. ###__Citation__ __In order to use these data, you must cite this data set with the following citation:__ Hugelius G, Bockheim JG, Camill, P, Elberling B, Grosse G, Harden JW, Johnson K, Jorgenson T, Koven C, Kuhry P, Michaelson G, Mishra U, Palmtag J, Ping C-L, O’Donnell J, Schirrmeister L, Schuur EAG, Sheng Y, Smith LC, Strauss J, Yu Z. (2013) A new dataset for estimating organic carbon storage to 3m depth in soils of the northern circumpolar permafrost region. Earth System Science Data, 5, 393–402, doi:10.5194/essd-5-393-2013.
Northern Circumpolar Soil Carbon Database, Alaska, geotiff- and netCDF-files
tab-delimited text (encoding: UTF-8: Unicode)
The SAR Surface Soil Moisture (SSM) product is derived from the ENVISAT ASAR sensor operating in Global Monitoring (GM) mode. The SAR SSM product is a weekly mean composite at 1 km spatial resolution for the years 2005 to 2011. The SAR Surface Soil Moisture parameter represents a relative measure of the soil moisture in the top layer of the soil, scaled between references corresponding approximately to wilting level (0%) and field capacity (100%). In addition to the SSM maps, quality indicator maps provide information on the number of individual SSM retrievals that were averaged in order to achieve the corresponding weekly SSM composites. More information about the image processing and SSM retrieval can be found in the product guide. This dataset is part of the ESA Data User Element (DUE) Permafrost Full Product Set (doi:10.1594/PANGAEA.780111).
Surface Soil Moisture from ENVISAT ASAR GM, 2005-2011, Mackenzie River Valley and Delta, Beaufort Sea Region (CA)
2012 CARVE campaign flights | Must sign in to ORNL DAAC to download
This data set provides atmospheric methane (CH4), carbon dioxide (CO2), and carbon monoxide (CO) dry air mole fractions and water vapor mole fractions (H2O) from continuous in situ measurements at the CARVE flux tower in Fox, Alaska between October 2011 and May 2015 for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Air was drawn from three different heights above the base of the tower (31.7 m, 17.1 m, and 4.9 m) and analyzed using a Picarro cavity ring-down spectrometer (CRDS). Measurements of ambient and sonic temperature, vertical and horizontal velocity, and atmospheric pressure are also included in the data set. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Miller, J.B., C. Sweeney, A. Karion, and C.E. Miller. 2017. CARVE: L2 Atmospheric CO2, CO, and CH4 Concentrations, CARVE Tower, Alaska, 2011-2015. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1419 This dataset is openly shared, without restriction, in accordance with the [EOSDIS Data Use Policy](https://earthdata.nasa.gov/earth-observation-data/data-use-policy?_ga=2.88701406.419703260.1676448349-1262327661.1663154735).
CARVE atmospheric gas concentrations, tower based, Alaska, 2012-2015
The global peatland distribution derived from PEATMAP (1200dpi)
The presented dataset contains chamber measurements of methane transport (mg CH₄ g dry plant mass-1 day-1) through individual aerenchymous peatland plants and the ancillary data for these measurements. Chamber measurements were performed for 7 plant species at two peatland sites, an oligotrophic fen and an ombrotrophic bog part of Siikaneva peatland complex in Southern Finland (61.8249° N, 24.1390° E, altitude 170 m a.s.l.) , during growing seasons 2013 and 2014 (between 1st of May and 28th of October). The ancillary data contains measurements of water table depth from the moss surface, air and peat temperature during the measurement as well as leaf area, dry mass of plant material, specific leaf area, number of leaves and the proportion of brown leaves in each sample that was measured. The dataset was collected to quantify the impact of plant species, plant properties and environmental factors on methane transport through aerenchymous plants. Plant CH₄ transport rate was measured using custom-made cylinder-shaped chambers that varied in volume between 0.7 and 5.0 liters. A plant sample of 2–104 leaves (depending on the growth form of the measured plant) belonging to the same species was separated from the peat and moss underneath by two plexiglass plates that were attached together with a hinge and had a smooth rubber seal between them to avoid compression of the plant. The proportion of green leaves in the sample varied from 0 to 100 % depending on the phase of the growing season. The sample was then covered with an opaque plastic chamber that was sealed with the plate by a smooth rubber seal attached to the bottom of the chamber. Airtightness of the system was ensured by tightening a belt that extended from one plate to the other over the chamber. Finally, a rubber stopper was used to seal a vent hole in the top of the chamber. Each plant sample was measured for 35 minutes, during which four 20 ml air samples were drawn from the chamber with a syringe through the rubber stopper in the top of the chamber at 5, 15, 25 and 35 minutes after chamber closure. The air samples were then injected into evacuated 12 ml glass vials (Labco Limited, UK). Simultaneous to the flux measurements, temperatures in the chamber (air) and peat at 5, 15 and 30 cm depth were recorded. WT was measured from a perforated plastic tube installed into the peat next to the sample after the WT level in the tube had stabilized for at least 30 minutes. After the flux measurement, the plant sample was cut with scissors and transported to the laboratory in a plastic bag. In each plant sample, the number of leaves was counted, the leaf area of brown and green leaf parts was measured with a scanner, and the dry weight was obtained for brown and green leaf parts separately after oven drying the sample at 60 °C for 24 hours. Using these data, specific leaf area (SLA, m²/g) was calculated for each sample. CH₄ concentration in the glass vials was analyzed with an Agilent Technologies 7890A gas chromatograph and Gilson GX-271 liquid handler. The CH₄ ﬂux was calculated as the linear change in CH~4~ concentration in relation to time, chamber volume and temperature. Nonlinear changes in CH₄ concentration that were visually detected, were surmised to have resulted from a leak in the chamber or in the vial and were excluded from the analysis. In total 6 % of the measurements were excluded from the final dataset due to such nonlinearities.
Chamber measured methane transport through individual aerenchymous plants at a boreal fen and a bog
Lara, M. J. et al. Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska. Sci. Data 5:180058 doi: 10.1038/sdata.2018.58 (2018).
CSV-file created from WP4 Mackenzie 2019 stable water isotopes __Citation:__ Juhls, Bennet; Bécu, Guislain; Oziel, Laurent; Leymarie, Edouard; Matsuoka, Atsushi; Lizotte, Martine; Ferland, Joannie; Béguin, Marine; Laberge-Carignan, Audrey; Guilmette, Caroline; Maury, Juliette; Hilborn, Andrea; Tisserand, Lucas; Devred, Emmanuel; Doxaran, David; Bossé-Demers, Thomas; Meyer, Hanno; Babin, Marcel (2021): Stable water isotopes (δ18O, δD, d-excess) in the surface water of the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937579, In: Juhls, Bennet; Lizotte, Martine; Matsuoka, Atsushi; Mével, Gaëlle; Bécu, Guislain; Overduin, Pier Paul; Devred, Emmanuel; Doxaran, David; Ferland, Joannie; Forget, Marie-Hélène; Hilborn, Andrea; Leymarie, Edouard; Maury, Juliette; Oziel, Laurent; Tisserand, Lucas; Miles, Dillon; Anikina, David Obie James; Guilmette, Caroline; Béguin, Marine; Couture, Raoul-Marie; Bossé-Demers, Thomas; Laberge-Carignan, Audrey; Chaillou, Gwénaëlle; Bélanger, Simon; Bruyant, Flavienne; Babin, Marcel (2021): Hydrographical, biogeochemical and biooptical water properties in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937587
NunaWP4Mackenzie19_stable_water_isotopes CSV
simulated soil temperature and modelled CO2 fluxes.txt
WP4 Mackenzie 2019 stable water isotopes
58 files in GeoTIFF (*.tif) format | Must sign in to ORNL DAAC to download
Land cover classification for Yukon Flats, Alaska, USA; the Peace-Athabasca Delta, Alberta; and the Canadian Shield, Northwest Territories (NWT), Canada
Taken from: Brosius, L. S., Walter Anthony, K. M., Grosse, G., Chanton, J. P., Farquharson, L. M., Overduin, P. P., and Meyer, H. (2012), Using the deuterium isotope composition of permafrost meltwater to constrain thermokarst lake contributions to atmospheric CH4 during the last deglaciation, J. Geophys. Res., 117, G01022, doi:10.1029/2011JG001810.
The dataset consists of one xlsx file with four sheets, listing (1) ¹³C and ¹⁴C data of litter and organic layers in northern Russia, Scandinavia, Canada and Alaska; (2) ¹³C and ¹⁴C data of mineral active layers and non-permafrost soils in Siberia; (3) ¹⁴C data of exposures of Holocene peat and thermokarst deposits in Siberia; and (4) ¹⁴C of exposures of Ice Complex deposits in Siberia, including coordinates of sampling sites and references to original publications. This dataset has been used to quantify the contribution of carbon from different terrestrial sources to dissolved and particulate organic carbon in the large Siberian rivers Ob, Yenisey, Lena and Kolyma. To achieve this, we have complemented publicly available long-term monitoring data with a new literature compilation in a statistical approach. The related monitoring dataset with concentrations of dissolved and particulate organic carbon in the Ob, Yenisey, Lena and Kolyma as well as their ¹³C and ¹⁴C contents is found under 'Water Quality' at: https://arcticgreatrivers.org/data/ Link to MATLAB code for statistical source apportionment: https://git.bolin.su.se/bolin/wild-2019 The data on terrestrial OC isotopes are compiled from previous studies. The references are next to the data in the tables. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Wild, B., Andersson, A., Bröder, L., Vonk, J., Hugelius, G., McClelland, J. W., Song, W., Raymond P. A., Gustafsson, Ö. (2019). Rivers across the Siberian Arctic unearth the patterns of carbon release from thawing permafrost. Proceedings of the National Academy of Sciences of the U.S.A. https://doi.org/10.1073/pnas.1811797116
Content of carbon isotopes (¹³C and ¹⁴C) in organic carbon pools in terrestrial Siberia
Water samples for stable isotopes were collected untreated in 10 mL HDPE vials, sealed tightly, stored in the dark at 4°C. Measurements were conducted at the laboratory facility for stable isotopes at AWI Potsdam using a Finnigan MAT Delta-S mass spectrometer equipped with equilibration units for the online determination of hydrogen and oxygen isotopic composition. The data is given as δD and δ18O values, which is the per mille difference to standard V-SMOW. The deuterium excess (d-excess) is calculated by: d-excess=δD-8.*δ18O. The measurement accuracy for hydrogen and oxygen isotopes was better than ±0.8%¸ and ±0.1%, respectively (Meyer et al., 2000; doi:10.1080/10256010008032939).
Stable water isotopes (δ18O, δD, d-excess) in the surface water during 4 expeditions from spring to fall in 2019, Mackenzie Delta Region (CA)
2014 CARVE campaign flights | Must sign in to ORNL DAAC to download
Measurement of CDOM absorption was conducted from a water sample within 12 hours of collection using an UltraPath liquid waveguide system (World Precision Instruments, Inc.) over the wavelengths ranging from 200 to 722 nm (see also Matsuoka et al. (2012; doi:10.5194/bg-9-925-2012) for details). To minimize temperature effects, both the sample and the reference water were kept at 4 °C for at least 30 minutes prior to analysis. We followed the International Ocean Colour Coordinating Group (IOCCG) Ocean Optics and Biogeochemistry CDOM protocols (Mannino et al., 2019 (see further details)) with a few modifications: 1) reference water with salinity ±2 relative to the sample was prepared on site a few hours before sample analysis to minimize the effect of difference in refractive index between sample and reference; 2) aCDOM(λ) was measured in flow mode, meaning, a measurement was made while water was running using a peristaltic pump (Lefering et al., 2017; doi:10.1364/AO.56.006357). While the use of a long optical cell provides a good better signal particularly withinin the visible spectral domain essential to SOCRS, it necessarily suffers from light saturation in the UV domain. To overcome this issue, an optimal length of a cell (i.e. 10 cm or 200 cm) was selected following an empirical relationship between optical density observed at 350 and 443 nm based on Matsuoka et al. (2012; doi:10.5194/bg-9-925-2012). For each sample, measurements were done in triplicates of which each was visually inspected for quality control. CDOM measurements were fitted using following equation: a_CDOM (λ)=a_CDOM (λ_0 )*e^(-S(λ-λ_0)), where S is the spectral slope of aCDOM(λ) between 350 and 500 nm (Babin et al., 2003; doi:10.1029/2001JC000882 and Matsuoka et al., 2012; doi:10.5194/bg-9-925-2012).
Colored dissolved organic matter absorption (aCDOM) and spectal slopes (S) in the surface water during 4 expeditions from spring to fall in 2019, Mackenzie Delta Region (CA)
CO2 and CH4 rates from incubations and potential fluxes.txt
Metadata for this dataset in ISO19139 format.
This dataset, called DARTS, comprises footprints of retrogressive thaw slump (RTS) and active layer detachments slides (ALD) identified and quantified using an automated deep learning approach in RTS hotspots across the Arctic and Subarctic permafrost regions. We utilized multispectral PlanetScope imagery with a spatial resolution of 3 meters (m), complemented by ArcticDEM (Digital Elevation Models) and derived datasets, including slope, relative elevation, and Landsat-derived change trends. The dataset covers an area of 1.6 million square-kilometers (km²), with at least one coverage between 2021 and 2023, and provides annual coverage for approximately 900,000 km². In several highly active key sites, such as Banks Island, Peel Plateau, and Novaya Zemlya, we extended the data frequency and temporal coverage to 2018-2023. We mapped a total of more than 43,000 individual RTS and ALD, many of them multiple times. We offer two levels of datasets; Level 1: RTS footprints per image with timestamps; and Level 2: annually aggregated RTS footprints. Essential metadata includes image footprints, dataset coverage, timestamps, and model-specific information. To enhance reproducibility and further use, the training labels, processing code, and model checkpoints are publicly available. This version, v1, is the first openly accessible release. The dataset will be maintained and continuously updated in both spatial and temporal extent. It can be used for mapping and quantifying RTS, analyzing spatio-temporal patterns of RTS dynamics, or serving as input for landscape dynamics models. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Ingmar Nitze, Konrad Heidler, Nina Nesterova, Jonas Küpper, Emma Schütt, Tobias Hölzer, Sophia Barth, Mark J Lara, Anna Liljedahl, & Guido Grosse. (2024). DARTS: Multi-year database of AI detected retrogressive thaw slumps (RTS) and active layer detachment slides (ALD) in hotspots of the circum-arctic permafrost region - v1. Arctic Data Center. doi:10.18739/A2RR1PP44.
DARTS: Multi-year database of AI detected retrogressive thaw slumps (RTS) and active layer detachment slides (ALD) in hotspots of the circum-arctic permafrost region - v1
Anthropogenic greenhouse gas (carbon dioxide, methane, nitrous oxide) data from inverse models and UNFCCC national inventories This dataset contains 5 datasets, including GHG data from inverse models and UNFCCC national inventories in the top emitter countries: 1. CO2_inversion_1990-2019: annual CO2 flux from from 6 inversion models in three sectors: 1. Land flux from all land 2. Land flux from managed land 3. Land flux from managed land by adjusting the lateral flux 2. CH4_inversion_2000-2017: CH4 flux from from 10 in-situ inversion (2000-2017) and 11 satellite inversion (2010-2017) models from four sectors: 1. Anthropogenic emissions from managed land. 2. Emissions from the fossil sector 3. Emissions from the agriculture and waste sector combined 4. Emissions from biomass burning 3. N2O_inversion_1997-2016: anthropogenic N2O emissions from from 3 models. 4. Inventory_1990-2019: inventory data collecting from UNFCCC national inventories. The classification of sectors is corresponding with the inversion data files for each gas specie. 5. Inventory_1990-2019_IPCC: inventory data collecting from UNFCCC national inventories in IPCC category. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Deng, Zhu, Ciais, Philippe, Tzompa-Sosa, Zitely, Saunois, Marielle, Chevallier, Frédéric, Tan, Chang, Sun, Taochun, Ke, Piyu, Cui, Yanan, & Liu, Zhu. (2021). GHG data from inverse models and UNFCCC national inventories v0.1 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5089799
GHG data from inverse models and UNFCCC national inventories, Global, 1990-2019
Taken from: Laurent, M., Fuchs, M., Herbst, T., Runge, A., Liebner, S., and Treat, C. C.: Relationships between greenhouse gas production and landscape position during short-term permafrost thaw under anaerobic conditions in the Lena Delta, Biogeosciences, 20, 2049–2064, https://doi.org/10.5194/bg-20-2049-2023, 2023.
SAR-based Lake Methane Ebullition Flux User Guide
Water samples were filtered through 0.7 µm GF/F filter, and acidified with 25 µL Suprapur HCl (10 M) on the same day of sampling. DOC samples were stored and kept at 4°C in the dark during transport until further analysis. Concentration of DOC was measured using high-temperature catalytic oxidation (TOC-VCPH, Shimadzu) at the Alfred-Wegener-Institute (AWI) Potsdam, Germany. Blanks (Milli-Q water) and certified reference standards (Battle-02, Mauri-09 or Super-05 from the National Laboratory for Environmental Testing, Canada) were measured for quality control.
Dissolved organic carbon (DOC) concentration in the surface water during 4 expeditions from spring to fall in 2019, Mackenzie Delta Region (CA)
WP4 Mackenzie 2019 particle absorption surface water
Regional Water Bodies, 2007-2011, Beaufort Sea Region
Full ensemble for 2009-2010 | Must sign in to ORNL DAAC to download
CSV file created from KoPF Vegetation mass __Citation:__ Shevtsova, Iuliia; Kruse, Stefan; Herzschuh, Ulrike; Brieger, Frederic; Schulte, Luise; Stuenzi, Simone Maria; Pestryakova, Luidmila A; Zakharov, Evgenii S (2023): Ground layer above-ground biomass of 39 sites in central Chukotka from 2018 - Raw data of dry weight for each sub-ground vegetation type sampling plot. PANGAEA, https://doi.org/10.1594/PANGAEA.956072
Widhalm, B et al. (2018): Classification of Tundra Regions with Polarimetric Terrasar-X Data. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium,22-27 July 2018, Valencia, Spain, 8551-8554, https://doi.org/10.1109/IGARSS.2018.8518283
The product provides modeled mean annual ground temperatures (MAGT) at the top of the permafrost for New Zealand at 1 km spatial resolution. Permafrost probability (fraction values from 0 to 1) is assigned to each grid cell with MAGT < 0°C. The mean MAGT was validated for the Northern Hemisphere using ground temperature from more than 920 boreholes available in GTN-P and TSP database and regional permafrost publications. The RMSE was 2.0 °C and the validation showed almost no cold or warm bias. The same modelling parameters as for the Northern Hemisphere were used for New Zealand but the results could not be validated due to the lack of borehole data. Modeled ground temperature data is also available for the Northern Hemisphere, the Antarctic, the Andes, and the East African Plateau. More Information about the modelling method can be found in the product guide.
Ground Temperature Map, 2000-2016, New Zealand
2012 Tower data | Must sign in to ORNL DAAC to download
This dataset provides in situ airborne measurements of atmospheric carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), and water vapor concentrations, plus air temperature, pressure, relative humidity, and wind speed values over Alaska and the Yukon and Northwest Territories of Canada during the Arctic Carbon Aircraft Profile (Arctic-CAP) monthly sampling campaigns from April-November 2017. Observations have been averaged to a 10-second interval and are reported with the number of samples (N) and standard deviation. During each of the six monthly campaigns, flights over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain included 25 vertical profiles, from the surface up to 6 km altitude, at locations selected to complement regular long-term vertical profiles, remote sensing data, and ground-based flux tower measurements. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Sweeney, C., and K. McKain. 2019. ABoVE: Atmospheric Profiles of CO, CO2 and CH4 Concentrations from Arctic-CAP, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1658 This dataset is openly shared, without restriction, in accordance with the [EOSDIS Data Use Policy](https://earthdata.nasa.gov/earth-observation-data/data-use-policy?_ga=2.88701406.419703260.1676448349-1262327661.1663154735).
ABoVE: Atmospheric Profiles of CO, CO2 and CH4 Concentrations from Arctic-CAP, 2017
Field investigations were performed in four areas: a treeless mountainous tundra (16-KP-04; Lake Rauchuagytgyn area), tundra-taiga transition zone (16-KP-01, Lake Ilirney area) and a northern taiga (18-BIL-01, 18-BIL-02). In total, 39 sites were investigated. The sites were placed to cover different vegetation communities that characterise central Chukotka. Fifteen-meter radius sample plots (sites) were demarcated in the most homogeneous locations. Heterogeneity was accommodated by roughly assorting vegetation into two to three vegetation types per sampling plot. Within each area of roughly estimated vegetation types we selected one 0.5 x 0.5 m subplot for representative ground-layer above-ground biomass (ABG) harvesting (major taxa and other). For moss and lichen AGB harvesting inside 0.5 x 0.5 m subplots representative 0.1 x 0.1 m subplots were chosen. All harvested AGB samples were weighed fresh in the field. In general, AGB samples with a weight of more than 15 g were subsampled. All samples were oven dried (60 °C, 24 h for ground-layer and moss and lichen samples, 48 h for shrub and tree branch samples) and weighed again. This dataset contains the raw data of dry weight for each sub-ground vegetation type sampling plot. All data was collected by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia. The AGB data calculations for the plot area including tree and tall shrubs can be found at https://doi.org/10.1594/PANGAEA.923719.
Ground layer above-ground biomass of 39 sites from 2018 central Chukotka (RU) - Raw data of dry weight for each sub-ground vegetation type sampling plot
CSV-file created from WP4 Mackenzie 2019 SPM TPC TPN __Citation:__ Lizotte, Martine; Juhls, Bennet; Bécu, Guislain; Oziel, Laurent; Leymarie, Edouard; Matsuoka, Atsushi; Ferland, Joannie; Béguin, Marine; Laberge-Carignan, Audrey; Guilmette, Caroline; Maury, Juliette; Hilborn, Andrea; Tisserand, Lucas; Devred, Emmanuel; Doxaran, David; Bossé-Demers, Thomas; Mével, Gaëlle; Bruyant, Flavienne; Babin, Marcel (2021): Suspended particulate matter (SPM), Total particulate carbon (TPC) and Total particulate nitrogen (TPN) concentrations in the surface water of the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937572, In: Juhls, Bennet; Lizotte, Martine; Matsuoka, Atsushi; Mével, Gaëlle; Bécu, Guislain; Overduin, Pier Paul; Devred, Emmanuel; Doxaran, David; Ferland, Joannie; Forget, Marie-Hélène; Hilborn, Andrea; Leymarie, Edouard; Maury, Juliette; Oziel, Laurent; Tisserand, Lucas; Miles, Dillon; Anikina, David Obie James; Guilmette, Caroline; Béguin, Marine; Couture, Raoul-Marie; Bossé-Demers, Thomas; Laberge-Carignan, Audrey; Chaillou, Gwénaëlle; Bélanger, Simon; Bruyant, Flavienne; Babin, Marcel (2021): Hydrographical, biogeochemical and biooptical water properties in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937587
Figure 2: Geospatial datasets representing the heterogeneity in both landform and NDVI across the ACP of northern Alaska. From: Lara, M., Nitze, I., Grosse, G. et al. Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska. Sci Data 5, 180058 (2018). https://doi.org/10.1038/sdata.2018.58 The tundra geomorphology map (a) was validated with 1000 reference sites (700 and 300 in the Arctic Peaty Lowlands and Arctic Sandy Lowlands, respectively) using 249 SPOT-5 ortho-tiles (b), while the NDVI trend map (c) was developed using between 40 to 110 image observations per 30 m pixel (d).
Figure 1 (Examples of ice wedge thermokarst pool expansion occurring over time spans of 5–9 years in Alaska, Canada, and Russia. Pools are outlined in red within high resolution panchromatic satellite imagery from four survey areas.) from Abolt, C. J., Atchley, A. L., Harp, D. R., Jorgenson, M. T., Witharana, C., Bolton, W. R., Schwenk, J., Rettelbach, T., Grosse, G., Boike, J., Nitze, I., Liljedahl, A. K., Rumpca, C. T., Wilson, C. J., & Bennett, K. E. (2024). Topography Controls Variability in Circumpolar Permafrost Thaw Pond Expansion. Journal of Geophysical Research: Earth Surface, 129(9), e2024JF007675. https://doi.org/10.1029/2024JF007675
Synthesis data of plant macrofossil records
We defined permafrost settlements as settlements located within the permafrost extent, as modelled by Obu et al. (2019). The permafrost extent is based on the modeled temperature at the top of the permafrost(TTOP model) for the period 2000-–2016. The permafrost extent is available at the circum-Arctic scale, with a resolution of 1 km2.The permafrost zones are defined as follows: continuous (90-–100% area coverage), discontinuous (50–-90% area coverage), and sporadic (0-–50% area coverage). To estimate the future of permafrost settlements, we used projected permafrost extents modeled by Hjort et al. (2018) using Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5 for the period 2041–2060. The model is binary and uses 30 arc-second grid cells (1 km2) to determine if permafrost is present or absent. We used the consensus index (Ic), that which classifies future permafrost extent into hazard zones (1, low; 2, medium; and 3, high hazard zones). When defining hazard zones, the consensus index considers the relative increase of the active layer thickness, ground ice content, ground temperature, permafrost thaw potential, surface properties (sediment/bedrock), fine-grained sediment content, frost susceptibility of ground material, and slope gradient. Some estimates made with linear interpolation technique for 509 settlements using 2002 census data. (Data of settlements over 500 inhabitants inside AHDR's definition on Arctic -77 estimates for 2002) Detailed information about the methods can be found in the [publication to which this dataset is a supplement](https://doi.org/10.1007/s11111-020-00370-6). ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Wang, Shinan, & Ramage, Justine. (2021). Settlements located within the permafrost extent in the Arctic Circumpolar Permafrost Region (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4519217
Settlements located within the permafrost extent in the Arctic Circumpolar Permafrost Region, 2017
This dataset contains v4.0 permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%). Case A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Westermann, S.; Barboux, C.; Bartsch, A.; Delaloye, R.; Grosse, G.; Heim, B.; Hugelius, G.; Irrgang, A.; Kääb, A.M.; Matthes, H.; Nitze, I.; Pellet, C.; Seifert, F.M.; Strozzi, T.; Wegmüller, U.; Wieczorek, M.; Wiesmann, A. (2024): ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v4.0. NERC EDS Centre for Environmental Data Analysis, 04 April 2024. doi:10.5285/93444bc1c4364a59869e004bf9bfd94a. https://dx.doi.org/10.5285/93444bc1c4364a59869e004bf9bfd94a
Permafrost extent for the Northern Hemisphere, v4.0 from MODIS LST, ERA5, 1997-2021
thaw depth, TOC in active layer, soil temperature 2016.txt
The effects of oxygen availability, freezing and thawing, temperature, and labile organic matter (OM) additions on greenhouse gas production were studied in northeast Siberian polygonal tundra soils, including the seasonally thawed active layer and upper perennially frozen permafrost. Soils were incubated at constant temperatures of 1 °C, 4 °C, or 8 °C for up to 150 days. CO2 production in surface layers was three times higher than in the deeper soil. Under anaerobic conditions, SOM decomposition was 2-6 times lower than under aerobic conditions and more CO2 than CH4 was produced. CH4 contributed less than 2% to anaerobic decomposition in thawed permafrost but more than 20% in the active layer. A freeze-thaw cycle caused a short-lived pulse of CO2 production directly after re-thawing. Q10 values, calculated via the equal-carbon method, increased with soil depth from 3.4 ± 1.6 in surface layers to 6.1 ± 2.8 in the permafrost. The addition of plant-derived labile OM (13C-labelled Carex aquatilis leaves) resulted in an increase in SOM decomposition only in permafrost (positive priming). The data files give laboratory data on three active layer and permafrost soils from the northeast Siberian Arctic tundra on Samoylov. The files contain CO2 and CH4 production in several incubations under aerobic and anaerobic conditions, at different temperatures, and with or without the additions of labile organic matter.
Soil and incubation data of permafrost-affected soils in Samoylov, 2011 & 2013
*ZIP containing Shapefiles from doi.pangaea.de/10.1594/PANGAEA.861733:* iryp_data_frames.shp iryp_field_photos.shp iryp_field_sites.shp QG1000k_quaternary_deposits_thickness.shp QG1000k_yedoma_russia.shp QG2500k_yedoma_russia.shp SG500k_yedoma_russia.shp SG7200k_yedoma_alaska.shp western_yukon_loess.shp
Lidar-derived original point clouds, 2014 chunk02
This repository includes data and code to accompany the manuscript 'Topography controls variability in circumpolar permafrost thaw pond expansion' by Abolt et al. The data include satellite imagery and derived maps of thermokarst pools from twenty-seven survey areas in North America and Siberia. The code, written in MATLAB (R2021a), contains demonstrations of the workflow for generating the maps. The demonstrations include training a generalized UNet for mapping thermokarst pools using data from three survey areas, 'fine tuning' the UNet for use at a specific survey area using transfer learning, applying a trained UNet to infer thermokarst pool extent within satellite imagery, and performing histogram matching as a pre-processing step to improve satellite imagery contrast. Contains MATLAB script files and M files, TIF files, shape files, XML, Excel, TXT, and CSV files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM). ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Abolt C ; Atchley A ; Harp D ; Rumpca C (2024): Maps of ice wedge thermokarst pool expansion from twenty-seven circumpolar survey areas. Next-Generation Ecosystem Experiments (NGEE) Arctic, ESS-DIVE repository. Dataset. doi:10.5440/1834773
Maps of ice wedge thermokarst pool expansion from twenty-seven circumpolar survey areas
This raster dataset, in Cloud Optimized GeoTIFF format (COG), provides information on land surface changes at the pan-arctic scale. Multispectral Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI imagery (cloud-cover less than 80%, months July and August) was used for detecting disturbance trends (associated with abrupt permafrost degradation) between 2003 and 2022. For each satellite image we calculated the Tasseled Cap multi-spectral index to translate the spectral reflectance signal to the semantic information Brightness, Greenness, and Wetness. In order to characterize change information, we calculated the linear trend of the Brightness, Greenness and Wetness over two decades on the individual pixel level. The final map product therefore contains information on the direction and magnitude of change for all three Tasseled Cap parameters in 30m spatial resolution across the pan-arctic permafrost domain. Features detected include coastal erosion, lake drainage, infrastructure expansion, and fires. The general processing methodology was developed by Fraser et al. 2014 and adapted and expanded by Nitze et al. 2016 and Nitze et al. 2018. Here we upscaled the processing to the circum-arctic permafrost region and the recent 20-year period from 2003 through 2022. The service covers the permafrost region up to 81° North: Alaska (USA), Canada, Greenland, Iceland, Norway, Sweden, Finland, Russia, Mongolia, and China. For Russia and China, regions not containing permafrost were excluded. The data has been processed in Google EarthEngine within the research projects ERC PETA-CARB, ESA CCI+ Permafrost, NSF Permafrost Discovery Gateway, and EU Arctic PASSION. The dataset is a contribution to the "Panarctic requirements-driven Permafrost Service" of the [Arctic PASSION project](https://arcticpassion.eu/). Changes in the Tasseled Cap indices Brightness, Greenness, and Wetness are displayed in the image bands red, green, and blue, respectively. Here, coastal erosion (a trend of a land surface transitioning to a water surface) is depicted in dark blue colors, while coastal accretion (a trend of a water surface transitioning to a land surface) is depicted in bright orange colors. Drained lakes appear in bright yellow or orange colors, depending on the soil conditions and vegetation regrowth. Fire scars are a further common feature, which can appear in different colors, depending on the time of the fire and pre-fire land cover. The data can be explored via the [Arctic Landscape EXplorer (ALEX)](https://alex.awi.de/) and is available as a public [web map service (WMS)](https://maps.awi.de/services/common/permafrost/wms?request=GetCapabilities&service=WMS), both hosted by Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research.
Pan-Arctic Visualization of Landscape Change (2003-2022), Arctic PASSION Permafrost Service
The regional waterbody classification is derived from ENVISAT ASAR Wide Swath data with 150 m resolution. Maps represent the extent of water bodies for the summer months July and August for the years 2007 to 2011. Water body maps indicate the presence of water (1) and no water (0). In addition, the number of measurements used to derive the water bodies product are also provided. Water body maps are also available are for the Alaska North Slope and Alaska Highway, the Mackenzie River Valley and Delta, and the Ob Estuary. More information about image processing and classification can be found in the product guide. **This dataset is part of the [ESA Data User Element (DUE) Permafrost Full Product Set](https://doi.pangaea.de/10.1594/PANGAEA.780111).**
Water Body Classification from ENVISAT ASAR Wide Swath, 2007-2011, Lena Delta / East Taymir / Cherskii, Laptev and East Siberian Sea Region (RU)
This dataset contains methane and nitrous oxide dissolved gas concentration, dissolved methane carbon isotope, and ancillary hydrographic data from research cruises in the North American Arctic Ocean between 2015-2018. Ocean samples for methane and nitrous oxide analysis were collected from Niskin bottles mounted on a CTD rosette. Water was collected into glass serum bottles and allowed to overflow three times before preserving with mercuric chloride and sealing with with butyl rubber stoppers and aluminum crimp seals. Gas concentrations were determined using a purge and trap system coupled to a gas chromatograph/mass spectrometer, following the method of Capelle et al. (2015). Equilibrium dry atmospheric concentrations were 328.25, 329.14, 330.11, and 330.96 ppb for N2O and 1919.64, 1933.67, 1934.92, and 1933.50 ppb for CH4 in 2015, 2016, 2017, and 2018, respectively. Equilibrium dissolved concentrations were calculated from the measured temperature and salinity following Wiesenburg and Guinasso (1979) for CH4 and Weiss and Price (1980) for N2O. Equilibrium concentrations were calculated based on sample temperature and salinity and the atmospheric N2O or CH4 concentrations measured at Barrow, Alaska by the NOAA Earth System Research Laboratory Global Monitoring Division (Dlugokencky et al., 2020a,b), with corrections to local sea level pressure and 100% humidity. Oxygen concentration was determined using an oxygen sensor mounted on the Niskin rosette, calibrated with discrete samples analyzed by Winkler titration. The mixed layer depth was defined based on a potential density difference criterion of 0.125 kg/m³ relative to the density at 5 m depth, using CTD profiles binned to 1 m. The mixed layer depth was set to 5 m as a minimum. The instantaneous gas transfer velocities and fluxes are based on the instantaneous wind speed at the time of sampling. The 30-day weighted gas transfer velocities and fluxes are integrated over the residence time of the gas in the mixed layer, using up to the prior 30 days of observations, following the method of Teeter et al. (2018) as described in the main manuscript of Manning et al. (2022). The 60-day weighted gas transfer velocities and fluxes are integrated over the residence time of the gas in the mixed layer, using the prior 60 days of observations, following the method of Teeter et al. (2018) as described in the main manuscript of Manning et al. (2022). Atmospheric sea level pressure was obtained from the NCEP/NCAR reanalysis product, which is provided by the NOAA-ESRL Physical Sciences Laboratory (https://psl.noaa.gov/data/gridded). Fractional ice cover was obtained from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (https://osi-saf.eumetsat.int). Sea ice concentration product AMSR-2 (identifier OSI-408) was used in 2017–2018 and SSMIS (identifier OSI-401-b) was used in 2015–2016.
Methane and nitrous oxide sea-air fluxes calculated from samples collected in the North American Arctic Ocean (2015–2018)
Material for the determination of suspended particulate matter (SPM), total particulate carbon (TPC) and total particulate nitrogen (TPN) was obtained by filtering 300-700 mL water samples on pre-weighed, precombusted (450°C for >5 hours) 47mm Whatmann GF/F (0.7 µm) filters. The filters were dried overnight at 60°C and vacuum-sealed for storage in petri dishes kept at room temperature until analysis. SPM was calculated as the difference in dry weight between individually-marked filters before and after filtration (Doxaran et al., 2012; doi:10.5194/bg-9-3213-2012). Concentrations of TPC and TPN were determined using a Carbon Hydrogen Nitrogen (CHN) analyzer (Perkin Elmer) following methods described in Doxaran et al. (2012; doi:10.5194/bg-9-3213-2012).
Suspended particulate matter (SPM), Total particulate carbon (TPC) and Total particulate nitrogen (TPN) concentrations in the surface water during 4 expeditions from spring to fall in 2019, Mackenzie Delta Region (CA)
Permafrost Probability Fraction (PERPROB) [Fraction, 0-1]
2017 Campaign Flights | Must sign in to ORNL DAAC to download
Maximum coverage of the dataset with at least one coverage between 2018-2023
The Quaternary geology map was created for the Yana-Indigirka (eastern part) and Kolyma lowlands, tundra and partly forest-tundra zones. The map corresponds to the nomenclature sheet of Government Geological Map R-(55)-57, northern part. The primary goal for creating the map is to enhance current knowledge of Quaternary deposits distribution in Yedoma regions in North-Eastern Siberia due to the lack of the geological maps of large scale for this region. For most area of the region maps of 1:200.000 scale are still not created and only geological maps of 1:1.000.000 scale exist. The Database of Quaternary Deposits from Maps of 1:1.000.000 scale, East and Central Siberia, is available via https://apgc.awi.de (Bryant et al., 2017, https://doi.org/10.5066/F7VT1Q89). The main Quaternary deposits on the Yana-Indigirka and Kolyma lowlands are late Pleistocene ice-rich Yedoma Ice Complex (LIII2-4), corresponded to yedoma uplands, and Holocene Alas Complexes (lbIV) formed in drained thermokarst lake basins (alases) due to the Yedoma IC thawing in the late Pleistocene-Holocene. High ice-rich Yedoma Ice Complex (IC) deposits are very vulnerable to observed climate warming, which lead to the landscape changes such as surface subsidence, thermokarst and thermoerosion processes activation, deepening of the active layer thickness, etc., as well as to remobilization of the buried freezelocked organic matter and contribution to greenhouse gases fluxes to the atmosphere. The map was created using Landsat images that allow the area of the yedoma uplands and alases to be clearly identified. The mapping of Quaternary deposits was done using Landsat TM 5 and 7 ETM+ satellite images acquired from August and July for the 2001-2010 period. Detecting of Quaternary deposits corresponded to landforms were conducted manually using spectral characteristics of satellite images, landform morphology, absolute heights and existing Quaternary Geology map of 1:1.000.000 scale. The working scale of the mapping was 1:30.000. Mapped Quaternary deposits are represented by six types: Yedoma IC, alas complex, alluvial, alluvial-marine, marine, and bedrocks. A comparison of the Quaternary deposit distribution as mapped with the Landsat satellite imagery and the 1:1.000.000 geological map revealed a 2.5-times overestimation of the Yedoma IC deposits extent area at the 1:1.000.000 scale. The Landsat-based map corresponds to 1:200.000 scale and provides important information about the distribution of yedoma uplands and thermokarst development during Holocene and is the basis for the analysis of yedoma-alas landscapes changes under modern climate warming. Detailed information about methods and results can be found in Veremeeva&Glushkova (2016) and Shmelev et al. (2017).
Quaternary deposits map of Yana-Indigirka and Kolyma lowlands tundra zone, R-55-57, based on Landsat imagery
This dataset includes maps of the locations and number of methane ebullition hotspots in 15 frozen lakes in the southern portion of the Goldstream Valley and the surrounding landscape just north of Fairbanks, Alaska, USA. Hotspots were identified from early winter high resolution aerial photographs acquired three days after lake-ice formation in October 2014. Hotspot ebullition seeps are defined as point-sources of high ebullition that release methane from lake sediments year-round. High rates of bubbling impede ice formation. In early winter, bubbling leads to dark, round open holes in lake ice which were visible in the aerial photos. This project investigated the role of theromkarst lakes in thawing of permafrost and mobilization of organic carbon in frozen soils. There are 15 data files included in this dataset as compressed shapefiles (*.shp). ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Anthony, K.W., P. Hanke, and P. Lindgren. 2021. ABoVE: Methane Ebullition Hotspots in Frozen Lakes near Fairbanks, Alaska, Oct 2014. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1861 This dataset is openly shared, without restriction, in accordance with the [EOSDIS Data Use Policy](https://earthdata.nasa.gov/earth-observation-data/data-use-policy?_ga=2.88701406.419703260.1676448349-1262327661.1663154735).
ABoVE: Methane Ebullition Hotspots in Frozen Lakes near Fairbanks, Alaska, Oct 2014
2003-2014 SCIAMACHY+GOSAT L3 V100 XCH4 NetCDF file
Taken from: Estop-Aragonés C, Cooper MDA, Fisher JP, Thierry A, Garnett MH, Charman DJ, Murton JB, Phoenix GK, Treharne R, Sanderson NK, Burn CR, Kokelj S V, Wolfe SA, Lewkowicz AG, Williams M, Hartley IP. (2018). Limited release of previously-frozen C and increased new peat formation after thaw in permafrost peatlands. Soil Biology and Biochemistry, 118, 115-129. https://doi.org/10.1016/j.soilbio.2017.12.010
CSV-file created from WP4 Mackenzie 2019 nutrients concentrations __Citation:__ Lizotte, Martine; Juhls, Bennet; Bécu, Guislain; Oziel, Laurent; Leymarie, Edouard; Matsuoka, Atsushi; Ferland, Joannie; Béguin, Marine; Laberge-Carignan, Audrey; Guilmette, Caroline; Maury, Juliette; Hilborn, Andrea; Tisserand, Lucas; Devred, Emmanuel; Doxaran, David; Bossé-Demers, Thomas; Deslongchamps, Gabrièle; Tremblay, Jean-Éric; Babin, Marcel (2021): Nutrient (NO3, NO2, PO4, SiO4) concentrations in the surface water of the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937578, In: Juhls, Bennet; Lizotte, Martine; Matsuoka, Atsushi; Mével, Gaëlle; Bécu, Guislain; Overduin, Pier Paul; Devred, Emmanuel; Doxaran, David; Ferland, Joannie; Forget, Marie-Hélène; Hilborn, Andrea; Leymarie, Edouard; Maury, Juliette; Oziel, Laurent; Tisserand, Lucas; Miles, Dillon; Anikina, David Obie James; Guilmette, Caroline; Béguin, Marine; Couture, Raoul-Marie; Bossé-Demers, Thomas; Laberge-Carignan, Audrey; Chaillou, Gwénaëlle; Bélanger, Simon; Bruyant, Flavienne; Babin, Marcel (2021): Hydrographical, biogeochemical and biooptical water properties in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937587
WP4 Mackenzie 2019 nutrients concentrations CSV
ACPR settlements permafrost zipped Shapefile
Lidar-derived original point clouds, 2012 chunk08
Zipped folder containing methane flux data
CSV-file created from WP4 Mackenzie 2019 temperature & salinity surface water __Citation:__ Bécu, Guislain; Juhls, Bennet; Oziel, Laurent; Leymarie, Edouard; Matsuoka, Atsushi; Lizotte, Martine; Ferland, Joannie; Babin, Marcel (2021): Temperature and salinity in the surface water of the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937571, In: Juhls, Bennet; Lizotte, Martine; Matsuoka, Atsushi; Mével, Gaëlle; Bécu, Guislain; Overduin, Pier Paul; Devred, Emmanuel; Doxaran, David; Ferland, Joannie; Forget, Marie-Hélène; Hilborn, Andrea; Leymarie, Edouard; Maury, Juliette; Oziel, Laurent; Tisserand, Lucas; Miles, Dillon; Anikina, David Obie James; Guilmette, Caroline; Béguin, Marine; Couture, Raoul-Marie; Bossé-Demers, Thomas; Laberge-Carignan, Audrey; Chaillou, Gwénaëlle; Bélanger, Simon; Bruyant, Flavienne; Babin, Marcel (2021): Hydrographical, biogeochemical and biooptical water properties in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937587
WP4 Mackenzie 2019 temperature & salinity surface water CSV
Taken from: Cooper, M., Estop-Aragonés, C., Fisher, J. et al. Limited contribution of permafrost carbon to methane release from thawing peatlands. Nature Clim Change 7, 507–511 (2017). https://doi.org/10.1038/nclimate3328
This dataset, titled the Synthesized Alaskan Tundra Field Database (SATFiD), provides a comprehensive collection of in-situ field data compiled from 37 existing datasets resulting from field surveys conducted at Alaska tundra sites between 1972 to 2020. The data were harmonized prior to being included in this dataset. The variables include active layer thickness, vegetation cover (by plant functional types), soil moisture and temperatures, as well as the wildfire history. SATFiD provides a unique lens into various long-term ecological processes within the tundra (such as the fire-permafrost-vegetation interactions) under a rapidly changing climate. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Chen, D., X. Zhu, M. Kogure, E.E. Hoy, X. Xu, N.H.F. French, L.T. Berner, A.L. Breen, S. Bret-Harte, S.J. Davidson, J.J. Ebersole, G.V. Frost, S.J. Goetz, R.E. Hewitt, J.K.Y. Hung, C.M. Iversen, G. Iwahana, R. Jandt, L.K. Jenkins, A.N. Kade, I. Klupar, T.V. Loboda, S. Ludwig, M.J. Macander, M.C. Mack, C.R. Meyers, R.J. Michaelides, E.A. Miller, S. Natali, T.W. Nawrocki, P.R. Nelson, A.D. Parsekian, E. Rastetter, M.K. Raynolds, A.V. Rocha, K. Schaefer, U. Schickhoff, E.A.G. Schuur, S. Tsuyuzaki, C.E. Tweedie, S.V. Zesati, D.A. Walker, P.J. Webber, M. Williams, and D. Zona. 2023. Field Data on Soils, Vegetation, and Fire History for Alaska Tundra Sites, 1972-2020. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2177
Field Data on Soils, Vegetation, and Fire History for Alaska Tundra Sites, 1972-2020
We defined permafrost settlements as settlements located within the permafrost extent, as modelled by Obu et al. (2019). The permafrost extent is based on the modeled temperature at the top of the permafrost(TTOP model) for the period 2000-–2016. The permafrost extent is available at the circum-Arctic scale, with a resolution of 1 km2.The permafrost zones are defined as follows: continuous (90-–100% area coverage), discontinuous (50–-90% area coverage), and sporadic (0-–50% area coverage). To estimate the future of permafrost settlements, we used projected permafrost extents modeled by Hjort et al. (2018) using Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5 for the period 2041–2060. The model is binary and uses 30 arc-second grid cells (1 km2) to determine if permafrost is present or absent. We used the consensus index (Ic), that which classifies future permafrost extent into hazard zones (1, low; 2, medium; and 3, high hazard zones). When defining hazard zones, the consensus index considers the relative increase of the active layer thickness, ground ice content, ground temperature, permafrost thaw potential, surface properties (sediment/bedrock), fine-grained sediment content, frost susceptibility of ground material, and slope gradient. Some estimates made with linear interpolation technique for 509 settlements using 2002 census data. (Data of settlements over 500 inhabitants inside AHDR's definition on Arctic -77 estimates for 2002) Detailed information about the methods can be found in the [publication to which this dataset is a supplement](https://doi.org/10.1007/s11111-020-00370-6). ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Wang, Shinan, & Ramage, Justine. (2021). Population in the Arctic Circumpolar Permafrost Region at settlement level (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4529610
Population in the Arctic Circumpolar Permafrost Region at settlement level, 2017
Taken From: Philben, M., Taş, N., Chen, H., Wullschleger, S. D., Kholodov, A., Graham, D. E., & Gu, B. (2020). Influences of hillslope biogeochemistry on anaerobic soil organic matter decomposition in a tundra watershed. Journal of Geophysical Research: Biogeosciences, 125, e2019JG005512. https://doi.org/10.1029/2019JG005512
Metadata: DARTS_Multi_year_database_of_AI_detected_retrogre.xml
Samples for bacterial abundance (1.5 mL) were preserved with glutaraldehyde (1% final concentration) and stored at -80°C. Samples were stained with SYBRTM Green I (Thermofisher Scientific) and analyzed on a flow cytometer (FACSCanto, BD Biosciences) as previously described (Gasol & Del Giorgio, 2000; doi:10.3989/scimar.2000.64n2197).
Bacteria cells in the surface water during 4 expeditions from spring to fall in 2019, Mackenzie Delta Region (CA)
Zipped shapefile of peatland distribution - Oceania
Footprints of RTS and ALD features, processed on individual image scenes
The regional waterbody classification is derived from ENVISAT ASAR Wide Swath data with 150 m resolution. Maps represent the extent of water bodies for the summer months July and August for the years 2007 to 2011. Water body maps indicate the presence of water (1) and no water (0). In addition, the number of measurements used to derive the water bodies product are also provided. Water body maps are also available are for the Alaska North Slope and Alaska Highway, the Laptev Sea Coast, and the Ob Estuary. More information about image processing and classification can be found in the product guide. **This dataset is part of the [ESA Data User Element (DUE) Permafrost Full Product Set](https://doi.pangaea.de/10.1594/PANGAEA.780111).**
Water Body Classification from ENVISAT ASAR Wide Swath, 2007-2011, Mackenzie River Valley and Delta, Beaufort Sea Region (CA)
__Figure 10__ Box plots of (a) chlorophyll a (Chl a) concentration (mg m−3) and (b) bacterial abundance (cells in mL−1) for the four WP4 Nunataryuk expeditions (Legs 1, 2, 3, and 4) in 2019. Boxes represent the interquartile range (IQR), the horizontal lines show the medians, while the vertical lines show data spread with min/max whiskers. Notice the y-axis log10 scale. Also note that a few data points below 100 K (Leg 2) were excluded from panel (b). __Citation:__ Lizotte, M. Juhls, B. et al. (2023). Nunataryuk field campaigns: Understanding the origin and fate of terrestrial organic matter in the coastal waters of the Mackenzie Delta region. Earth System Science data. Earth Syst. Sci. Data, 15, 1617–1653. https://doi.org/10.5194/essd-15-1617-2023
Zipped shapefile of peatland distribution - Africa
GIS shape files including spatial and morphometric data of pingos in the western Canadian Arctic and accompanying Excel spreadsheet summarizing metrics for pingos of differing height categories. Within ArcGIS®, the pingo database of Wolfe et al. (2021) and HRDEM (Natural Resources Canada, 2020) were used to determine pingo metrics, including top and base elevations, planimetric (“footprint”) area, slope, surface area, ratio of the surface area to base area (i.e., surface ratio), and volume. Pingos were seperated into categories of <0.6 m high, for which no additional metrics were calculated, and pingos >0.6 to 2.0 m, and >2.0 m high. Metrics were determined with use of custom algorithm implemented as a Python script utilizing the ArcPy library and DEM Surface Tools extension (Jenness, 2010) for ArcGIS® DesktopTM 10.7.1. Detailed methodology may be found in the accompanying paper. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Wolfe, Stephen; Morse, Peter; Parker, Ryan; Phillips, Marcus (2023), “Distribution and morphometry of pingos, western Canadian Arctic, Northwest Territories, Canada: Datasets and Supplementary Materials”, Mendeley Data, V1, doi: 10.17632/kgbsjvrj32.1
Distribution and morphometry of pingos, western Canadian Arctic, Northwest Territories, (CA)
Surface Soil Moisture from ENVISAT ASAR GM, 2005-2010, Laptev Sea Coast, Laptev and East Siberian Sea Region (RU)
Lidar-derived original point clouds, 2012 chunk05
Metadata: BAWLD_CH4_Methane_Fluxes_from_Boreal_and_Arctic.xml
The Terrestrial Multidisciplinary distributed Observatories for the Study of the Arctic Connections (T-MOSAiC) 2021 permafrost thaw data set provides standardized measurements for permafrost thaw and the linked parameters snow depth, vegetation height, water level and soil texture as suggested by Boike et al., 2021. It contains measurements from eight circumpolar permafrost sites where data was obtained using the myThaw app which is based on the user-friendly, multiparameter protocol for standardized monitoring of permafrost thaw for the year 2021. The data set contains detailed metadata for each site including data on the timing of data collection, geographical coordinates and land surface characteristics (vegetation, ground surface and water features).
T-MOSAiC 2021 myThaw data set, Circum-Arctic
Methane data from Barter Island 03.09.2017
R-55-57 Quaternary Geological Map geojson
Taken from: Tanski, G., Wagner, D., Knoblauch, C., Fritz, M., Sachs, T., & Lantuit, H. (2019). Rapid CO2 release from eroding permafrost in seawater. Geophysical Research Letters, 46, 11244– 11252. https://doi.org/10.1029/2019GL084303
Centre for Environmental Data Analysis (CEDA)
CSV-file created from WP4 Mackenzie 2019 FDOM __Citation:__ Matsuoka, Atsushi; Juhls, Bennet; Bécu, Guislain; Oziel, Laurent; Leymarie, Edouard; Lizotte, Martine; Ferland, Joannie; Béguin, Marine; Laberge-Carignan, Audrey; Guilmette, Caroline; Maury, Juliette; Hilborn, Andrea; Tisserand, Lucas; Devred, Emmanuel; Doxaran, David; Bossé-Demers, Thomas; Gonçalves-Araujo, Rafael; Stedmon, Colin A; Babin, Marcel (2021): Fluorescent dissolved organic matter (FDOM) intensity (Parafac components and FDOM indices) in the surface water of the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937582, In: Juhls, Bennet; Lizotte, Martine; Matsuoka, Atsushi; Mével, Gaëlle; Bécu, Guislain; Overduin, Pier Paul; Devred, Emmanuel; Doxaran, David; Ferland, Joannie; Forget, Marie-Hélène; Hilborn, Andrea; Leymarie, Edouard; Maury, Juliette; Oziel, Laurent; Tisserand, Lucas; Miles, Dillon; Anikina, David Obie James; Guilmette, Caroline; Béguin, Marine; Couture, Raoul-Marie; Bossé-Demers, Thomas; Laberge-Carignan, Audrey; Chaillou, Gwénaëlle; Bélanger, Simon; Bruyant, Flavienne; Babin, Marcel (2021): Hydrographical, biogeochemical and biooptical water properties in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937587
The dataset consists of respiration and methane production rates obtained from soil microcosm studies carried out under controlled temperature and incubation conditions. Soils represent the low- and high-centered polygon active layers and permafrost (when present) from the NGEE-Arctic Intensive Study Site 1. The data file contains the results of the incubation study for six cores: NGADG0017, NGADG0073, NGADG0005, NGADG0009, NGADG0048, NGADG0043. Core and soil characteristics are provided along with the incubation conditions, replicates, and CO2 and CH4 results. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Taniya RoyChowdhury, David Graham, Stan Wullschleger. 2016. CO2 and CH4 Production in Low-Temperature Soil Incubations from Low and High Centered Polygons, Barrow, Alaska, 2012-2013. Next Generation Ecosystem Experiments Arctic Data Collection, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee, USA. Dataset accessed on [INSERT_DATE] at https://doi.org/10.5440/1237703.
CO2 and CH4 Production in Soil Incubations from Low and High Centered Polygons, Barrow, Alaska, 2012-2013
Figure 1. from Spangenberg, I., Overduin, P. P., Damm, E., Bussmann, I., Meyer, H., Liebner, S., Angelopoulos, M., Biskaborn, B. K., Grigoriev, M. N., and Grosse, G.: Methane pathways in winter ice of a thermokarst lake–lagoon–coastal water transect in north Siberia, The Cryosphere, 15, 1607–1625, https://doi.org/10.5194/tc-15-1607-2021, 2021.
CSV-file created from WP4 Mackenzie 2019 particle absorption surface water __Citation:__ Matsuoka, Atsushi; Juhls, Bennet; Bécu, Guislain; Oziel, Laurent; Leymarie, Edouard; Lizotte, Martine; Ferland, Joannie; Béguin, Marine; Laberge-Carignan, Audrey; Guilmette, Caroline; Maury, Juliette; Hilborn, Andrea; Tisserand, Lucas; Devred, Emmanuel; Doxaran, David; Bossé-Demers, Thomas; Babin, Marcel (2021): Particle absorption (aP) in the surface water of the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937581, In: Juhls, Bennet; Lizotte, Martine; Matsuoka, Atsushi; Mével, Gaëlle; Bécu, Guislain; Overduin, Pier Paul; Devred, Emmanuel; Doxaran, David; Ferland, Joannie; Forget, Marie-Hélène; Hilborn, Andrea; Leymarie, Edouard; Maury, Juliette; Oziel, Laurent; Tisserand, Lucas; Miles, Dillon; Anikina, David Obie James; Guilmette, Caroline; Béguin, Marine; Couture, Raoul-Marie; Bossé-Demers, Thomas; Laberge-Carignan, Audrey; Chaillou, Gwénaëlle; Bélanger, Simon; Bruyant, Flavienne; Babin, Marcel (2021): Hydrographical, biogeochemical and biooptical water properties in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937587
WP4 Mackenzie 2019 particle absorption surface water CSV
A .clr file is a space-delimited text file that defines the pixel value and the red, green, and blue color values.
This dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e. "concentrations") from flights over Alaska and the Yukon and Northwest Territories of Canada during the Arctic Carbon Aircraft Profile (Arctic-CAP) monthly sampling campaigns from April-November 2017. The data were derived from laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard the aircraft. During each of the six monthly campaigns, flights over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain included 25 vertical profiles, from the surface up to 6 km altitude, at locations selected to complement regular long-term vertical profiles, remote sensing data, and ground-based flux tower measurements. Measurements were initiated by the aircraft pilot at predetermined locations within each profile in order to evenly distribute flask sampling points throughout each flight. A total of 408 flask samples were collected during 55 individual flights. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost. Whole air samples collected in the PFPs were analyzed on automated systems at the NOAA Earth System Research Laboratory (ESRL) Global Monitoring Division and the Stable Isotope Laboratory of the Institute of Arctic and Alpine Research (INSTAAR) at the University of Colorado Boulder, which also analyze samples from the NOAA/ESRL Global Greenhouse Gas Reference Network. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Sweeney, C., K. McKain, B.R. Miller, and S.E. Michel. 2019. ABoVE: Atmospheric Gas Concentrations from Airborne Flasks, Arctic-CAP, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1717 This dataset is openly shared, without restriction, in accordance with the [EOSDIS Data Use Policy](https://earthdata.nasa.gov/earth-observation-data/data-use-policy?_ga=2.88701406.419703260.1676448349-1262327661.1663154735).
ABoVE: Atmospheric Gas Concentrations from Airborne Flasks, Arctic-CAP, 2017
requirement to sign up for a PANGAEA user account before downloading the ZIP
CSV-file created from WP4 Mackenzie 2019 aCDOM slope surface water __Citation:__ Matsuoka, Atsushi; Juhls, Bennet; Bécu, Guislain; Oziel, Laurent; Leymarie, Edouard; Lizotte, Martine; Ferland, Joannie; Béguin, Marine; Laberge-Carignan, Audrey; Guilmette, Caroline; Maury, Juliette; Hilborn, Andrea; Tisserand, Lucas; Devred, Emmanuel; Doxaran, David; Bossé-Demers, Thomas; Babin, Marcel (2021): Colored dissolved organic matter absorption (aCDOM) and spectal slopes (S) in the surface water of the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937580, In: Juhls, Bennet; Lizotte, Martine; Matsuoka, Atsushi; Mével, Gaëlle; Bécu, Guislain; Overduin, Pier Paul; Devred, Emmanuel; Doxaran, David; Ferland, Joannie; Forget, Marie-Hélène; Hilborn, Andrea; Leymarie, Edouard; Maury, Juliette; Oziel, Laurent; Tisserand, Lucas; Miles, Dillon; Anikina, David Obie James; Guilmette, Caroline; Béguin, Marine; Couture, Raoul-Marie; Bossé-Demers, Thomas; Laberge-Carignan, Audrey; Chaillou, Gwénaëlle; Bélanger, Simon; Bruyant, Flavienne; Babin, Marcel (2021): Hydrographical, biogeochemical and biooptical water properties in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937587
WP4 Mackenzie 2019 aCDOM slope surface water CSV
Taken from: Knoblauch, C et al. (2015): Regulation of methane production, oxidation, and emission by vascular plants and bryophytes in ponds of the northeast Siberian polygonal tundra. Journal of Geophysical Research: Biogeosciences, 120(12), 2525-2541, https://doi.org/10.1002/2015JG003053
Methane (CH4) production, oxidation, and emission were studied in ponds of the permafrost-affected polygonal tundra in northeast Siberia. Summer CH4 fluxes were measured with closed chambers at the margins of ponds vegetated by vascular plants and in their centers without vascular plants. Furthermore, CH4 and oxygen concentration gradients, stable carbon isotope signatures of dissolved and emitted CH4, and microbial CH4 production and CH4 oxidation were determined. The data files give the basic field and laboratory data on five ponds in the northeast Siberian Arctic tundra on Samoylov. The files contain water and soil temperature data of the ponds, methane fluxes, measured with closed chambers in the centres without vascular plants and the margins with vascular plants, the contribution of plant mediated fluxes on total methane fluxes, the gas concentrations (methane and dissolved inorganic carbon, oxygen) in the soil and the water column of the ponds, microbial activities (methane production, methane oxidation, aerobic and anaerobic carbon dioxide production), total carbon pools in the different horizons of the bottom soils, soil bulk density, soil substance density, and soil porosity.
Methane fluxes, soil gas profiles and microbial activities in ponds at Samoylov, 2002 & 2013
CARVE: Atmospheric gas concentrations user guide
Lidar-derived original point clouds, 2012 chunk07
CSV file derived and adapted from Shapefile (Wang, Shinan, & Ramage, Justine. (2021). Population in the Arctic Circumpolar Permafrost Region at settlement level (Version 2) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4519205)
Lake change datasets for 2017 and 2018 based on high-temporal resolution PlanetScope imagery as Polygon Shapefiles and csv
CSV file derived and adapted from Shapefile (Wang, Shinan, & Ramage, Justine. (2021). Settlements located within the permafrost extent in the Arctic Circumpolar Permafrost Region (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4519217)
WP4 Mackenzie 2019 nutrients concentrations
Study sites in two versions: a) including seawater and b) clipped to land area
173 vertical column netCDF files (*.nc) | Must sign in to ORNL DAAC to download
This data set provides total vertical column O2, CO2, CH4, CO, and H2O, as well as dry-air columns of CO2, CH4, CO, and H2O from airborne campaigns over the Alaskan and Canadian Arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). The data represent the Level 2 Quick Retrieval (L2QR) data product collected using the CARVE Fourier Transform Spectrometer (FTS). Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Dupont, F., F. Tanguay, M. Li, G. Perron, C.E. Miller, S.J. Dinardo, and T.P. Kurosu. 2017. CARVE: L2 Column Gas and Uncertainty from Airborne FTS, Alaska, 2012-2015. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1429 This dataset is openly shared, without restriction, in accordance with the [EOSDIS Data Use Policy](https://earthdata.nasa.gov/earth-observation-data/data-use-policy?_ga=2.88701406.419703260.1676448349-1262327661.1663154735).
CARVE column gas from Airborne fourier transform spectrometer, Alaska, 2012-2015
These are the data files for the Boreal-Arctic Wetland and Lake Database methane (CH4) flux dataset (BAWLD-CH4). The dataset is a synthesis of reported methane fluxes from natural ecosystems in the Boreal-Arctic region. The dataset is split into two components- 1) terrestrial methane fluxes (wetland and non-wetland) and 2) aquatic methane fluxes (lakes and ponds). BAWLD-CH4 was constructed in parallel with a compatible land cover dataset (Olefeldt et al. 2021) sharing the same land cover classes to enable refined bottom-up assessments of annual CH4 emissions from the Boreal-Arctic region. BAWLD-CH4 includes information on site-level, surface CH4 fluxes, but also on study design (measurement method, timing, and frequency) and site characteristics (vegetation, climate, hydrology, soil, and sediment types, permafrost conditions, lake size and depth, and BAWLD land cover class). ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ McKenzie Kuhn, Ruth Varner, David Bastviken, Patrick Crill, Sally MacIntyre, et al. 2021. BAWLD-CH4: Methane Fluxes from Boreal and Arctic Ecosystems. Arctic Data Center doi:10.18739/A2DN3ZX1R.
BAWLD-CH4: Methane Fluxes from Boreal and Arctic Ecosystems, 1984-2019, Pan-Arctic
Measurements of column-averaged dry-air mixing ratios (XCO2) from the CO2 Sounder Lidar instrument | Must sign in to ORNL DAAC to download
Field investigations were performed in the central Lena Delta. In total, 25 sites were investigated. The sites were chosen to represent a variety of vegetation communities of the investigated region. 30 m² sample plots (sites) were demarcated in the most homogeneous locations. Heterogeneity was accommodated by roughly assorting vegetation into two to three vegetation types per sampling plot. Within each area of roughly estimated vegetation types we selected one 0.5 x 0.5 m subplot for representative ground-layer above-ground biomass (ABG) harvesting (major taxa and other). For moss and lichen AGB harvesting inside 0.5 x 0.5 m subplots representative 0.1 x 0.1 m subplots were chosen. All harvested AGB samples were weighed fresh in the field. In general, AGB samples with a weight of more than 15 g were subsampled. All samples were oven dried (60 °C, 24 h and weighed again). All ground-layer vegetation AGB assessments were calculated for the fifteen-meter radius plot in g/m² for each sample plot, see https://doi.pangaea.de/10.1594/PANGAEA.935923. All data was collected by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany.
Ground layer above-ground biomass of 25 sites from 2018 central Lena Delta (RU) - Raw data of dry weight for each sub-ground vegetation type sampling plot
The data publication contains supplementary data to the article "Supplementary Dataset to: The catastrophic thermokarst lake drainage events of 2018 in northwestern Alaska: Fast-forward into the future". This data publication includes four datasets: 1. Lake change datasets for 1999-2014 and 2017-2018 based on Landsat and Sentinel-1 data as Polygon Shapefiles 2. Lake change datasets for 2017 and 2018 based on high-temporal resolution PlanetScope imagery as Polygon Shapefiles and csv. 3. Lake ice simulations for the study area for 1980-2018. 4. Study sites in two versions: a) including seawater and b) clipped to land area. Files are Polygon Shapefiles. The datasets cover the land area of the Baldwin Peninsula and northern Seward Peninsula in north-western Alaska. The datasets are (#1&#2) remote sensing based observations and (#3) modelled data. Methods are described in detail in the original manuscript (open access). Dataset #4 is the extent of the study site in two versions, a) full extent including seawater and b) land only including lakes. The land boundary was clipped with the “Global Self-consistent, Hierarchical, High-resolution Geography Database” (GSHHG; Wessel and Smith, 1996) dataset in scale “h”. The datasets cover different temporal periods and have a different temporal resolution. Data were collected to measure the extent of a rapid and widespread thermokarst lake drainage event in northwestern Alaska in 2018 and to compare the affected number of lakes and area to previous periods. Lake-ice model data were calculated to simulate lake-ice conditions since 1980 and to put the lake-ice and weather conditions in 2017/2018 into context.
Spatial lake dynamics and lake-ice datasets of the Northern Seward and Baldwin Peninsulas in Alaska
Taken from: Roy Chowdhury, T., Herndon, E.M., Phelps, T.J., Elias, D.A., Gu, B., Liang, L., Wullschleger, S.D. and Graham, D.E. (2015), Stoichiometry and temperature sensitivity of methanogenesis and CO2 production from saturated polygonal tundra in Barrow, Alaska. Glob Change Biol, 21: 722–737. doi:10.1111/gcb.12762
Data were analyzed to study the role of an winter ice cover for methane cycles of three different stages in the lake-lagoon-shelf transition in a region of rapidly thawing permafrost in northeast Siberia. Dissolved methane concentrations and stable carbon isotopic signature of methane as well as hydrochemical parameters, as stable water isotope composition, electrical conductivity, dissolved organic carbon and temperature were analyzed in ice cores. The ice cores were taken at Tiksi Bay (core 27-30), Polar Fox Lagoon (core 31 and 32), and Goltsovoye Lake (core 20-24) in April 2017.
Dissolved methane and hydrochemical parameters in ice cores from 2017, Bykovsky Peninsula, Lena Delta
Taken from: Sweeney, C., and K. McKain. 2019. ABoVE: Atmospheric Profiles of CO, CO2 and CH4 Concentrations from Arctic-CAP, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1658
Vertical profiles of downwelling irradiance (Ed) and upwelling radiance (Lu) were measured during legs 2, 3, and 4 using a Compact-Optical Profiling System (C-OPS) in an ICE-Pro frame from Biospherical Instruments, Inc. (for a detailed description see Morrow et al. 2010 (see further details)). Additionally, above-surface incident downward irradiance (Es(0+)) was measured at about two meters above sea level and was used to correct in-water Ed and Lu for changes in the incident light field during Lu profiling (Zibordi et al., 2019; doi:10.25607/OBP-691). All radiometric quantities were measured at 19 wavelengths spanning from 380 to 875 nm. In-water profiles were obtained from the boat using a 3 m long pole, deployed towards the sun to avoid shading from the boat. The data that were acquired with a tilt of more than 5 degrees were discarded (Hooker et al., 2013; doi:10.5194/bg-10-4511-2013). Due to the high absorption and scattering coefficients in the sampled waters and considering relatively large dimensions of the ICE-Pro, self-shading correction was not negligible. Absorption observed in the present study were mostly outside the limits examined by Gordon and Ding (1992; doi:10.4319/lo.1992.37.3.0491), suggesting the application of the correction questionable. To overcome this issue, we performed Monte-Carlo simulations using the SimulO software (Leymarie et al., 2010; doi:10.1364/AO.49.005415) for examining the self-shading correction factor on Lu at null depth (Gerbi et al., 2016 (doi:10.1175/JTECH-D-16-0067.1); Leymarie et al., 2018 (doi:10.3389/fmars.2018.00437)). The exact dimensions of the ICE-Pro were simulated and virtually placed at a depth of 0.5 m. A wide range of IOPs was considered to cover the conditions encountered in the field. The simulations provide a robust relationship between the computed self-shading and the quantity x = a + bb, where a is the total measured absorption coefficient (i.e., the contributions of pure water, CDOM, algal and non-algal particles) and bb is the total backscattering coefficient (i.e., the contributions of water molecules and particles). The shade-corrected upwelling radiance (Lu corrected) can be expressed as a function of the measured radiance (Lu measured) as: (1) L_u(corrected)=(L_u(measured))/((1-ε)), (2) ε=1-e^(-0.14*(a+bb)), where (2) is fitted for solar zenith angles > 45° and bb (which was not measured in the field) was calculated using an empirical relationship from the Malina-cruise dataset (Doxaran et al., 2012 (doi:10.5194/bg-9-3213-2012); Massicotte et al., 2020 (doi:10.5194/essd-13-1561-2021)). Subsurface downward irradiance and upward radiance Ed(0-) and Lu(0-) were estimated with an iterative linear fitting of the log-transformed Ed(z) and Lu(z) vs depth z. Fitting was applied to successively greater depths until the correlation coefficient (r2) exceeded 0.99 or until the layer thickness reached 2.5 m (Bélanger et al., 2017; doi:10.1175/JTECH-D-16-0176.1). Remote Sensing Reflectance (Rrs) was calculated following Mobley (1999; doi:10.1364/AO.38.007442) with: Rrs(λ)=(0.54*L_u (0^-,λ))/(E_s(0^+,λ)). To calculate the Rrs we used the R "Cops" package (https://github.com/belasi01/Cops) (Bélanger 2017: doi:10.1175/JTECH-D-16-0176.1).
Remote sensing reflectance (Rrs) of the surface water during 4 expeditions from spring to fall in 2019, Mackenzie Delta Region (CA)
excel sheet containing information for the 520 extracted thermokarst lagoons
Taken from: Abshire, J.B., J. Mao, H. Riris, S.R. Kawa, and X. Sun. 2022. ABoVE/ASCENDS: Active Sensing of CO2, CH4, and Water Vapor, Alaska and Canada, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2050
CSV-file created from WP4 Mackenzie 2019 POC PON __Citation:__ Lizotte, Martine; Juhls, Bennet; Bécu, Guislain; Oziel, Laurent; Leymarie, Edouard; Matsuoka, Atsushi; Ferland, Joannie; Béguin, Marine; Laberge-Carignan, Audrey; Guilmette, Caroline; Maury, Juliette; Hilborn, Andrea; Tisserand, Lucas; Devred, Emmanuel; Doxaran, David; Bossé-Demers, Thomas; Bröder, Lisa; Vonk, Jorien E; Babin, Marcel; Mével, Gaëlle (2021): Particulate organic carbon (POC) and particulate organic nitrogen (PON) concentrations in the surface water of the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937575, In: Juhls, Bennet; Lizotte, Martine; Matsuoka, Atsushi; Mével, Gaëlle; Bécu, Guislain; Overduin, Pier Paul; Devred, Emmanuel; Doxaran, David; Ferland, Joannie; Forget, Marie-Hélène; Hilborn, Andrea; Leymarie, Edouard; Maury, Juliette; Oziel, Laurent; Tisserand, Lucas; Miles, Dillon; Anikina, David Obie James; Guilmette, Caroline; Béguin, Marine; Couture, Raoul-Marie; Bossé-Demers, Thomas; Laberge-Carignan, Audrey; Chaillou, Gwénaëlle; Bélanger, Simon; Bruyant, Flavienne; Babin, Marcel (2021): Hydrographical, biogeochemical and biooptical water properties in the Mackenzie Delta Region during 4 expeditions from spring to fall in 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.937587
Zipped file containing tab delineated txt files.
Field investigations were performed in the central Lena Delta. In total, 25 sites were investigated. The sites were chosen to represent a variety of vegetation communities of the investigated region. 30 m2 sample plots (sites) were demarcated in the most homogeneous locations. Heterogeneity was accommodated by roughly assorting vegetation into two to three vegetation types per sampling plot. Within each area of roughly estimated vegetation types we selected one 0.5 x 0.5 m subplot for representative ground-layer above-ground biomass (ABG) harvesting (major taxa and other). For moss and lichen AGB harvesting inside 0.5 x 0.5 m subplots representative 0.1 x 0.1 m subplots were chosen. All ground-layer vegetation AGB assessments were calculated for the fifteen-meter radius plot in g m2 for each sample plot. All harvested AGB samples were weighed fresh in the field. In general, AGB samples with a weight of more than 15 g were subsampled. All samples were oven dried (60 °C, 24 h and weighed again). All data was collected by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany.
Total above-ground biomass of 25 sites from 2018 central Lena Delta (RU)
We synthesized 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 maps of the previous Yedoma coverage estimates, 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.
Database of Ice-Rich Yedoma Permafrost (IRYP)
Content of carbon isotopes (¹³C and ¹⁴C) in organic carbon pools in terrestrial Siberia Summary This dataset represents a literature compilation of carbon isotope data of four terrestrial organic carbon pools, (1) litter and organic layers in northern Russia, Scandinavia, Canada and Alaska (¹³C and ¹⁴C); (2) mineral active layers and non-permafrost soils in Siberia (¹³C and ¹⁴C); (3) exposures of Holocene peat and thermokarst deposits in Siberia (¹⁴C); (4) exposures of Ice Complex deposits in Siberia (¹⁴C). The dataset has been used to quantify the contribution of carbon from different sources to dissolved and particulate organic carbon in large Siberian rivers using statistical source apportionment. Web address https://bolin.su.se/data/wild-2019 Citations Wild, B., Andersson, A., Bröder, L., Vonk, J., Hugelius, G., McClelland, J. W., Song, W., Raymond P. A., Gustafsson, Ö. (2019). Rivers across the Siberian Arctic unearth the patterns of carbon release from thawing permafrost. Proceedings of the National Academy of Sciences of the U.S.A. doi:10.1073/pnas.1811797116 Principal Investigator Birgit Wild, Örjan Gustafsson Keywords Permafrost; Peat; Thermokarst; Active layer; Siberia Category Terrestrial > Arctic carbon Comments This dataset has been used to quantify the contribution of carbon from different terrestrial sources to dissolved and particulate organic carbon in the large Siberian rivers Ob, Yenisey, Lena and Kolyma. To achieve this, we have complemented publicly available long-term monitoring data with a new literature compilation in a statistical approach. The related monitoring dataset with concentrations of dissolved and particulate organic carbon in the Ob, Yenisey, Lena and Kolyma as well as their ¹³C and ¹⁴C contents is found under 'Water Quality' at: https://arcticgreatrivers.org/data/. Link to MATLAB code for statistical source apportionment: https://git.bolin.su.se/bolin/wild-2019. Contact information Email Address birgit.wild@aces.su.se Postal address Birgit Wild Department of Environmental Science and Analytical Chemistry (ACES), Stockholm University SE-106 91 Stockholm Sweden Metadata GCMD Science keywords Earth science > Land surface > Soils > Carbon GCMD Location Continent > Asia Status Completed Data Set Language English Project This study was funded by the US National Science Foundation (Grants 0229302, 0732821, and 1107774), the Swedish Research Council Vetenskapsrådet (Contracts 621-2013-5297 and 2017-01601 and 330-2014-6417), and the European Research Council (ERC-AdG CC-TOP Project 695331 and ERC-StG THAWSOME Project 676982). The European Union Horizon 2020 project Nunataryuk (Contract 773421) is additionally acknowledged. Description The dataset consists of one xlsx file with four sheets, listing (1) ¹³C and ¹⁴C data of litter and organic layers in northern Russia, Scandinavia, Canada and Alaska; (2) ¹³C and ¹⁴C data of mineral active layers and non-permafrost soils in Siberia; (3) ¹⁴C data of exposures of Holocene peat and thermokarst deposits in Siberia; and (4) ¹⁴C of exposures of Ice Complex deposits in Siberia, including coordinates of sampling sites and references to original publications. Publisher Bolin Centre for Climate Research, Stockholm University Dataset Version 1.0 Use Limitations None, but please cite Wild et al. (2019) Access Constraints Free Published 2019-05-06 15:46:16
Lidar-derived original point clouds, 2012 chunk03
generated from T-MOSAiC 2021 data tab-delimited text __Citation:__ Martin, Julia; Boike, Julia; Chadburn, Sarah; Zwieback, Simon; Anselm, Norbert; Goldau, Maybrit P; Hammar, Jennika; Abramova, Ekatarina N; Lisovski, Simeon; Coulombe, Stéphanie; Dakin, Brampton; Wilcox, Evan James; Giamberini, Mariasilvia; Radar, Fieke; Suominen, Otso; Rudd, Daniel Alexander; Mastepanov, Mikhail; Young, Amanda (2023): T-MOSAiC 2021 myThaw data set. PANGAEA, https://doi.org/10.1594/PANGAEA.956039
thaw depth, TOC in active layer, soil temperature 2019.txt
Lidar-derived original point clouds, 2014 chunk03
T-MOSAiC 2021 myThaw data set Metadata document
2013 Tower data | Must sign in to ORNL DAAC to download
Mean Annual Ground Temperature (MAGTM) [C°]
The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2, CH4, N2O, HF, CO, H2O, and HDO, are retrieved. This is the GGG2020 data release of observations from the TCCON station at Sodankylä, Finland ###__Citation__ Kivi, R., Heikkinen, P., & Kyrö, E. (2022). TCCON data from Sodankylä (FI), Release GGG2020.R0 (Version R0) [Data set]. CaltechDATA. https://doi.org/10.14291/tccon.ggg2020.sodankyla01.R0 Please review the [TCCON data use policy](https://tccon-wiki.caltech.edu/Main/DataUsePolicy) before downloading TCCON data.
TCCON data from Sodankylä, Lapland (FI), Release GGG2020.R0
This dataset provides the results of laboratory soil microcosm incubation experiments conducted under anaerobic conditions with and without the addition of ammonium chloride (NH4Cl). The microcosms contained soil collected in April 2017 from the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) near Intensive Site 9 (“Toeslope”) and Intensive Site 5 (“Plateau”). Incubations were conducted at 8°C for 55 days, with repeated measurements of headspace greenhouse gas concentrations. Microcosms were destructively harvested and extracted using ultrapure water and 0.1 M KCl for analysis of the porewater geochemistry. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Michael Philben, David Graham, Baohua Gu. 2020. Greenhouse Gas Production and Soil Chemistry in Anaerobic Soil Microcosm Incubations after Nitrogen Addition, Teller Road Site, Seward Peninsula, 2018-2019. Next Generation Ecosystem Experiments Arctic Data Collection, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee, USA. Dataset accessed on [INSERT_DATE] at https://doi.org/10.5440/1529003.
Greenhouse gas production and soil chemistry in soil Incubations after nitrogen addition, Seward Peninsula, 2018-2019
This dataset contains methane and nitrous oxide dissolved gas concentration and dissolved methane carbon isotope data from rivers in the Canadian Arctic Archipelago region collected between 2017-2019. River samples were collected during CCGS Amundsen cruises using a helicopter to travel to the rivers. At each river site, river water was pumped through tubing using a peristaltic pump. Water was collected into glass serum bottles and allowed to overflow three times before preserving with mercuric chloride and sealing with butyl rubber stoppers and aluminum crimp seals. Gas concentrations were determined using a purge and trap system coupled to a gas chromatograph/mass spectrometer, following the method of Capelle et al. (2015). For methane 𝛿13C, samples (one sample per event) were pre-concentrated through a purge and trap system (Finnigan PreCon Trace Gas Pre-Concentrator) and measured with a Finnigan Delta XP Plus mass spectrometer following the method of Damm et al. (2015).
Methane and nitrous oxide dissolved gas concentrations from rivers in the North American Arctic (2017–2019)
Lake ice simulations for the study area for 1980-2018
from: Nitze, Ingmar; Cooley, Sarah W; Duguay, Claude R; Jones, Benjamin M; Grosse, Guido (2020): The catastrophic thermokarst lake drainage events of 2018 in northwestern Alaska: fast-forward into the future. The Cryosphere, 14(12), 4279-4297, https://doi.org/10.5194/tc-14-4279-2020
Northern Seward Peninsula zipped shapefile
PEATMAP is a GIS shapefile dataset that shows a distribution of peatlands that covers the entire world. It was produced by combining the most high quality available peatland map from a wide variety of sources that describe peatland distributions at global, regional and national levels. The following sequence of comparisons to discriminate between overlapping data sources were used: (1) Relevance. The most important criterion was that source data are able to identify peatlands faithfully and to distinguish them from other land cover types, especially non-peat forming wetlands. (2) Spatial resolution. In areas where two or more overlapping data sources were indistinguishable in terms of their relevance to peatlands, the dataset with the finest spatial resolution was selected. (3) Age. In any areas where two or more overlapping datasets were indistinguishable based on both their apparent relevance to peatlands and their spatial resolution, the data product that had been most recently updated was selected. Recently updated products commonly contain much older source data, the period over which the latest revision source data were collected as the primary measure of the age of a dataset. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Xu, Jiren and Morris, Paul J. and Liu, Junguo and Holden, Joseph (2017) PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. University of Leeds. [Dataset] https://doi.org/10.5518/252
PEATMAP - peatland distribution maps based on meta-analysis, 1999-2010, Global
Vegetation shifts in circum-Arctic permafrost peatlands drive feedbacks with important consequences for peatland carbon budgets and the extent of permafrost thaw under changing climate. Recent shrub expansion across Arctic tundra environments has led to an increase in above-ground biomass, but the long-term spatiotemporal dynamics of shrub and tree growth in circum-Arctic peatlands remain unquantified. We investigate changes in peatland vegetation composition during the Holocene using previously-published plant macrofossil records from 76 sites across the circum-Arctic permafrost zone. In particular, we assess evidence for peatland shrubification at the continental scale. We identify increasing abundance of woody vegetation in circum-Arctic peatlands from ~8000 years BP to present, coinciding with declining herbaceous vegetation and widespread Sphagnum expansion. Ecosystem shifts varied between regions and present-day permafrost zones, with late-Holocene shrubification most pronounced where permafrost coverage is presently discontinuous and sporadic. After ~600 years BP, we find a proliferation of non-Sphagnum mosses in Fennoscandia and across the present-day continuous permafrost zone; and rapid expansion of Sphagnum in regions of discontinuous and isolated permafrost as expected following widespread fen-bog succession, which coincided with declining woody vegetation in eastern and western Canada. Since ~200 years BP, both shrub expansion and decline were identified at different sites across the pan-Arctic, highlighting the complex ecological responses of circum-Arctic peatlands to post-industrial climate warming and permafrost degradation. Our results suggest that shrubification of circum-Arctic peatlands has primarily occurred alongside surface drying, resulting from Holocene climate shifts, autogenic peat accumulation, and permafrost aggradation. Future shrubification of circum-Arctic peatlands under 21st century climate change will likely be spatially heterogeneous, and be most prevalent where dry microforms persist. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Richard E. Fewster, Paul J. Morris, Graeme T. Swindles, Ruza F. Ivanovic, Claire C. Treat, Miriam C. Jones, Holocene vegetation dynamics of circum-Arctic permafrost peatlands, Quaternary Science Reviews, Volume 307, 2023, 108055, ISSN 0277-3791, https://doi.org/10.1016/j.quascirev.2023.108055
Holocene vegetation dynamics of circum-Arctic permafrost peatlands
This dataset contains fluxes of carbon dioxide and methane on an active flood plain in the Siberian Lena River Delta during the growing seasons 2014 and 2015 by means of the eddy covariance method. For explaining the pronounced spatiotemporal variability in the carbon fluxes, a set of environmental parameters has been utilised. This data set may help to refine estimates of the pan-Arctic greenhouse gas budget and the reduction of its uncertainties.
Eddy covariance carbon fluxes and ancillary data, Samoylov Island, Lena River Delta, 2014-2015
Fluorescent dissolved organic matter intensity was measured with an Aqualog®. Measurements were corrected for inner-filter effects and for the Raman and Rayleigh scattering (Murphy et al., 2013; doi:10.1039/C3AY41160E). The different fluorescent components of DOM were isolated from combined signal by PARAFAC modeling using the “drEEM Toolbox” and following the recommendation of Murphy et al. (2013; doi:10.1039/C3AY41160E). The DOM components derived from PARAFAC modeling were compared with PARAFAC components from other studies through the OpenFluor database (Murphy et al., 2014; doi:10.1039/C3AY41935E). The coble-peaks indicate major fluorescent components (Coble 1996; doi:10.1016/0304-4203(95)00062-3) in marine FDOM EEMs (Excitation-Emission-Matrix). Peaks T represents protein-like compounds (tyrosine and tryptophane), peaks A and C are indicators of humic-like components whereas peak M was associated to marine humic-like fluorescence. The fluorescence index (FI) is calculated as the ratio of fluorescence at emission 450 nm and 500 nm, at fixed excitation of 370 nm. The HIX index is the ratio of the areas of two spectral wavelength regions in the emission spectra for an excitation at 254 nm and it is obtained as: HIX = H∕L, where H is the area between 435 and 480 nm in the emission spectra and L is the area in the emission spectra between 300 and 345 nm (Zsolnay et al., 1999; doi:10.1016/S0045-6535(98)00166-0). The BIX index is obtained by calculating the ratio of the emission at 380 and 430 nm, excited at 310 nm: BIX = IEm380∕IEm430 (Huguet et al., 2009; doi:10.1016/j.orggeochem.2009.03.002).
Fluorescent dissolved organic matter (FDOM) intensity (Parafac components and FDOM indices) in the surface water during 4 expeditions from spring to fall in 2019, Mackenzie Delta Region (CA)
Lidar-derived original point clouds, 2012 chunk02
Taken from: Oberle, Ferdinand K; Gibbs, Ann E; Richmond, Bruce M; Erikson, Li H; Waldrop, Mark P; Swarzenski, Peter W (2019): Towards determining spatial methane distribution on Arctic permafrost bluffs with an unmanned aerial system. SN Applied Sciences, 1(236), https://doi.org/10.1007/s42452-019-0242-9
Oberle, Ferdinand K; Gibbs, Ann E; Richmond, Bruce M; Erikson, Li H; Waldrop, Mark P; Swarzenski, Peter W (2019): Towards determining spatial methane distribution on Arctic permafrost bluffs with an unmanned aerial system. SN Applied Sciences, 1(236), https://doi.org/10.1007/s42452-019-0242-9
Zipped shapefile of peatland distribution - Europe
The regional waterbody classification is derived from ENVISAT ASAR Wide Swath data with 150 m resolution. Maps represent the extent of water bodies for the summer months July and August for the years 2007 to 2011. Water body maps indicate the presence of water (1) and no water (0). In addition, the number of measurements used to derive the water bodies product are also provided. Water body maps are also available are for the Alaska North Slope and Alaska Highway, the Mackenzie River Valley and Delta, and the Laptev Sea Coast. More information about image processing and classification can be found in the product guide. **This dataset is part of the [ESA Data User Element (DUE) Permafrost Full Product Set](https://doi.pangaea.de/10.1594/PANGAEA.780111).**
Water Body Classification from ENVISAT ASAR Wide Swath, 2007-2011, Ob Estuary and Yamal Peninsula, Western Siberia (RU)
2015 CARVE campaign flights | Must sign in to ORNL DAAC to download
This dataset contains a high-resolution land cover classification focused on water and wetland vegetation classes over three NASA ABoVE Campaign regions: Yukon Flats, Alaska, USA; the Peace-Athabasca Delta, Alberta; and the Canadian Shield, Northwest Territories (NWT), Canada. The product was derived from L-band synthetic aperture radar (SAR) acquisitions from the airborne NASA UAVSAR instrument in 2017-2019. The classification was trained and validated from field visits, UAV images, satellite imagery as well as other ABoVE datasets. Classifications in all regions are provided as both preliminary 13-class versions and final, simplified 5-class versions. Training and test data used for the classifier are also included as well as characteristics of lakes in the study area. This land cover classification was developed to support a project focusing on potential methane emissions from the shallow near-shore, or littoral, regions of lakes. The emergent aquatic vegetation classes can be used as a proxy for these littoral zones. Wetland vegetation classifications are provided as gridded raster files with an approximately 5-meter spatial resolution and aligned with the original UAVSAR footprints. Composite mosaics that aggregate these UAVSAR scenes by region and day of acquisition, if applicable, are also provided. Classifications in all regions are provided as both preliminary 13-class versions and final 5-class versions. There are 79 data files included in this dataset, 58 in GeoTIFF (*.tif) format and 21 compressed shapefiles (*.shp). ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Kyzivat, E.D., L.C. Smith, C. Huang, C. Wang, T. Langhorst, J.V. Fayne, M.E. Harlan, Y. Ishitsuka, D. Feng, L.H. Pitcher, and T.M. Pavelsky. 2021. ABoVE: Lake and Wetland Classification from L-band SAR, Alaska and Canada, 2017-2019. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1883 This dataset is openly shared, without restriction, in accordance with the [EOSDIS Data Use Policy](https://earthdata.nasa.gov/earth-observation-data/data-use-policy?_ga=2.88701406.419703260.1676448349-1262327661.1663154735).
ABoVE: Lake and Wetland Classification from L-band SAR, Alaska and Canada, 2017-2019
Measurements of methane (CH4) from the Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument.| Must sign in to ORNL DAAC to download
Taken from: Kyzivat, E.D., L.C. Smith, C. Huang, C. Wang, T. Langhorst, J.V. Fayne, M.E. Harlan, Y. Ishitsuka, D. Feng, L.H. Pitcher, and T.M. Pavelsky. 2021. ABoVE: Lake and Wetland Classification from L-band SAR, Alaska and Canada, 2017-2019. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1883
Surface Soil Moisture from ENVISAT ASAR GM, 2005-2011, Yakutsk, Central Yakutia (RU)
WP4 Mackenzie 2019 aCDOM slope surface water
This data set provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), ozone (O3), and water vapor (H2O) concentrations from airborne campaigns over the Alaskan and Canadian arctic for the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). These data are merged and gap-filled outputs from two different cavity ring-down spectrometers (CRDS; Picarro Inc.) flown aboard the CARVE aircraft and are presented at 5-second intervals throughout each flight. Aircraft latitude, longitude, and altitude are also provided. Flight campaigns took place from 2012 to 2015 between the months of March and November to enable investigation of both seasonal and inter-annual variability in atmospheric gas content. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Budney, J.W., R.Y-W. Chang, R. Commane, B.C. Daube, A. Dayalu, S.J. Dinardo, E.W. Gottlieb, A. Karion, J.O.W. Lindaas, C.E. Miller, J.B. Miller, S. Miller, M. Pender, J.V. Pittman, J. Samra, C. Sweeney, S.C. Wofsy, and B. Xiang. 2016. CARVE: L2 Merged Atmospheric CO2, CO, O3 and CH4 Concentrations, Alaska, 2012-2015. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1402 This dataset is openly shared, without restriction, in accordance with the [EOSDIS Data Use Policy](https://earthdata.nasa.gov/earth-observation-data/data-use-policy?_ga=2.88701406.419703260.1676448349-1262327661.1663154735).
CARVE atmospheric gas concentrations, airborne spectrometer, Alaska, 2012-2015
This dataset provides wetland methane (CH4) emissions, their uncertainties and underlying CH4 flux densities north from 45 N using three different wetland maps. The data products are derived using data from several eddy covariance CH4 flux sites, random forest machine learning algorithms and three prescribed wetland maps. The data are at 0.5 by 0.5 deg or 1 by 1 deg resolution, depending on the wetland map used. The dataset covers years 2013 and 2014. CH4 flux densities are provided only for grid cells with > 5 % wetland coverage. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Peltola, O., Vesala, T., Gao, Y., Räty, O., Alekseychik, P., Aurela, M., Chojnicki, B., Desai, A. R., Dolman, A. J., Euskirchen, E. S., Friborg, T., Göckede, M., Helbig, M., Humphreys, E., Jackson, R. B., Jocher, G., Joos, F., Klatt, J., Knox, S. H., Kowalska, N., Kutzbach, L., Lienert, S., Lohila, A., Mammarella, I., Nadeau, D. F., Nilsson, M. B., Oechel, W. C., Peichl, M., Pypker, T., Quinton, W., Rinne, J., Sachs, T., Samson, M., Schmid, H. P., Sonnentag, O., Wille, C., Zona, D., and Aalto, T.: Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations, Earth Syst. Sci. Data, 11, 1263–1289, https://doi.org/10.5194/essd-11-1263-2019, 2019.
Gridded monthly methane emissions from terrestrial wetlands, north of 45 °N, 2013-2014
Environmental Information Data Centre (EIDC)
Land and Wetland Classification User Guide PDF
We mosaicked twelve LandSat-8 OLI satellite images taken during the summer of 2014, which were used in an object based image analysis (OBIA) to classify the landscape. We mapped seventeen of the most dominant geomorphic land cover classes on the Alaskan Coastal Plain (ACP): value, class name 0 = No Data | 2 = Coalescent low-center polygons | 3 = Low-center polygons | 4 = Nonpatterned Drained Thaw Lake Basins | 7 = Drained slope | 8 = Flat-center polygons | 9 = High-center polygons | 10 = Sandy barrens | 11 = Sand dunes | 12 = Ice | 13 = Coastal saline waters | 16 = Rivers | 17 = Urban (i.e. towns and roads) | 18 = Riparian corridors | 20 = Ponds | 21 = Small lakes | 22 = Medium lakes | 23 = Large lakes ### __Credits__ This work was supported through the Next-Generation Ecosystem Experiments (NGEE-Arctic) project supported by the Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science, and the Integrated Ecosystem Model for Alaska and Northwest Canada project supported by the USGS Alaska Climate Science Center and by the Arctic, Western, and Northwest Boreal Landscape Conservation Cooperatives Detailed information about the methods can be found in the [publication to which this dataset is a supplement](https://doi.org/10.1038/sdata.2018.58). ###__Citation__ __In order to use these data, you must cite this data set with the following citation:__ Lara, M. J. SNAP Data Portal https://doi.org/10.21429/C9JS8S (2017)
Polygonal Tundra Geomorphology Map from Landsat time series, 2014, Alaskan Arctic Coastal Plain (US)
Lidar-derived original point clouds, 2014 chunk01
FLUXNET-CH4 Community Product Metadata CSV file
Taken from: Korrensalo, Aino; Mammarella, Ivan; Alekseychik, Pavel; Vesala, Timo; Tuittila, Eeva-Stiina (2021): Plant mediated methane efflux from a boreal peatland complex. Plant and Soil, https://doi.org/10.1007/s11104-021-05180-9
The dataset comprises in situ CO2 and CH4 fluxes, active layer depths, thawed carbon pools and soil temperatures from different non-vegetated soils in an active thaw slump in the Lena Delta, Siberia, Russia, measured in July 2016 and July 2019. - CO2 and CH4 rates from incubations and potential fluxes.txt: This dataset contains rates of CO2 and CH4 production and the potential CO2 and CH4 emission rates calculated from these incubation fluxes - in situ CO2 and CH4 chamber fluxes.txt: This dataset contains CO2 and CH4 fluxes measured with closed chambers from different sites on Kurungnakh in July 2016 and July 2019 - simulated soil temperature and modelled CO2 fluxes.txt: This dataset contains daily mean soil temperature data simulated with JSBACH for 2016 and the annual CO2 fluxes simulated with a Q10 model and the Introductory Carbon Balance Model (ICBM) - thaw depth, TOC in active layer, soil temperature 2016.txt: This dataset contains the thaw depth, TOC pools in the active layer and the soil temperature during the measurement period in July 2016 - thaw depth, TOC in active layer, soil temperature 2019.txt: This dataset contains the thaw depth, TOC pools in the active layer and the soil temperature during the measurement period in July 2019 ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Knoblauch, Christian, Beer, Christian, Schuett, Alexander, Sauerland, Lewis, Liebner, Susanne, Steinhof, Axel, Rethemeyer, Janet, Grigoriev, Mikhail N., & Faguet, Alexey. (2021). Incubation data, CO2 and CH4 flux data and soil properties of thaw slump soils on Kurungnakh, Lena Delta in July 2016 and July 2019 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5584710
Incubation data, Carbon flux data and soil properties of thaw slump soils on Kurungnakh, Lena Delta, 2016 & 2019
During Leg 1, the CTD (CTD RBR Maestro) was manually lowered in the water through an ice hole. During legs 2 to 4, the CTD (CTD RBR Concerto) was installed on a Seabird Scientific optical package frame. Poor quality profiles, that had been affected by ice-covered sensors, were removed. Atmospheric pressure observed at weather stations near the sampling locations (Aklavik, Inuvik, Shingle Point and Tuktoyaktuk) was used to tare the CTD pressure sensors. The salinity was re-measured on discrete water samples (Sal Lab) using Mettler Toledo Conductivity/Salinity/pH meter.
Temperature and salinity in the surface water during 4 expeditions from spring to fall in 2019, Mackenzie Delta Region (CA)
In a warming Arctic, permafrost-related disturbances, such as retrogressive thaw slumps (RTS), are becoming more abundant and dynamic, with serious implications for permafrost stability and bio-geochemical cycles on local to regional scales. Despite recent advances in the field of earth observation, many of these have remained undetected as RTS are highly dynamic, small, and scattered across the remote permafrost region. Here, we assessed the potential strengths and limitations of using deep learning for the automatic segmentation of RTS using PlanetScope satellite imagery, ArcticDEM and auxiliary datasets. We analyzed the transferability and potential for pan-Arctic upscaling and regional cross-validation, with independent training and validation regions, in six different thaw slump-affected regions in Canada and Russia. We further tested state-of-the-art model architectures (UNet, UNet++, DeepLabv3) and encoder networks to find optimal model configurations for potential upscaling to continental scales. The best deep learning models achieved mixed results from good to very good agreement in four of the six regions (maxIoU: 0.39 to 0.58; Lena River, Horton Delta, Herschel Island, Kolguev Island), while they failed in two regions (Banks Island, Tuktoyaktuk). Of the tested architectures, UNet++ performed the best. The large variance in regional performance highlights the requirement for a sufficient quantity, quality and spatial variability in the training data used for segmenting RTS across diverse permafrost landscapes, in varying environmental conditions. With our highly automated and configurable workflow, we see great potential for the transfer to active RTS clusters (e.g., Peel Plateau) and upscaling to much larger regions. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Nitze, I.; Heidler, K.; Barth, S.; Grosse, G. Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps. Remote Sens. 2021, 13, 4294. https://doi.org/10.3390/rs13214294
Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps (CA, RU)
Taken From: Anthony, K.W., P. Hanke, and P. Lindgren. 2021. ABoVE: Methane Ebullition Hotspots in Frozen Lakes near Fairbanks, Alaska, Oct 2014. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1861
Regional Water Bodies, 2007-2008, Ob Estuary
Metadata: Maps_of_ice_wedge_thermokarst_pool_expansion_from.xml
This dataset provides Synthetic Aperture Radar (SAR) estimates of lake-source methane ebullition flux in mg CH4/m2/d for thousands of lakes in five regions across Alaska. The study regions include the Atqasuk, Barrow Peninsula, Fairbanks, northern Seward Peninsula, and Toolik. L-band SAR backscatter values for early winter lake ice scenes were collected from 2007 to 2010 over 5,143 lakes using the Phased Array type L-band Synthetic Aperture Radar (PALSAR) instrument on the Advanced Land Observing Satellite (ALOS-1) satellite. The backscatter data were combined with field measurements of methane ebullition from 48 study lakes across the five regions to obtain a volumetric flux estimate for each lake. Mean methane gas-fractions from each region were applied to the SAR-based volumetric fluxes to obtain an estimate of methane ebullition mass flux per lake. The data files contain lake perimeters and the lake-specific attributes of lake area, SAR backscatter values and standard errors, volumetric flux with standard errors, mean percent of methane from gas samples, and methane ebullition mass flux. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Engram, M.J., K.W. Anthony, and F.J. Meyer. 2020. ABoVE: SAR-based Methane Ebullition Flux from Lakes, Five Regions, Alaska, 2007-2010. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1790 This dataset is openly shared, without restriction, in accordance with the [EOSDIS Data Use Policy](https://earthdata.nasa.gov/earth-observation-data/data-use-policy?_ga=2.88701406.419703260.1676448349-1262327661.1663154735).
SAR-based Methane Ebullition Flux from Lakes, Five Regions, Alaska, 2007-2010
Nitze, I.; Heidler, K.; Barth, S.; Grosse, G. Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps. Remote Sens. 2021, 13, 4294. https://doi.org/10.3390/rs13214294
Surface Soil Moisture from ENVISAT ASAR GM, 2005-2011, Ob Estuary and Yamal Peninsula, Western Siberia (RU)
TCVIS-2003-2022 Mosaic West Cloud Optimized GeoTIFF (COG)
CSV-file created from lake hydrochemistry NW-CA TAB (fixed longitude errors) __Citation:__ Fritz, Michael; Lantuit, Hugues; Wetterich, Sebastian (2021): Inorganic and organic hydrochemistry of lakes and ponds in continuous permafrost landscapes in the western Canadian Arctic. PANGAEA, https://doi.org/10.1594/PANGAEA.935012
This dataset contains v4.0 permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness. Case A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Westermann, S.; Barboux, C.; Bartsch, A.; Delaloye, R.; Grosse, G.; Heim, B.; Hugelius, G.; Irrgang, A.; Kääb, A.M.; Matthes, H.; Nitze, I.; Pellet, C.; Seifert, F.M.; Strozzi, T.; Wegmüller, U.; Wieczorek, M.; Wiesmann, A. (2024): ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v4.0. NERC EDS Centre for Environmental Data Analysis, 24 April 2024. doi:10.5285/d34330ce3f604e368c06d76de1987ce5. https://dx.doi.org/10.5285/d34330ce3f604e368c06d76de1987ce5
Permafrost active layer thickness for the Northern Hemisphere, v4.0 from MODIS LST, ERA5, 1997-2021
Rockglaciers and other periglacial processes were mapped using visual analysis of differential SAR interferograms and satellite optical images (Google Satellite, Bing Aerial) between 1998 and 2016. Every object is assigned a process type (rockglacier, landslide etc.) and state-of-activity. State-of-activity indicates surface deformation rates in cm per year. ### Acknowledgement Thanks to the University of Fribourg for its contributions to this inventory.
Rockglacier and Slope Instability Inventory from differential SAR interferograms and optical imagery, 1998-2016, Western Swiss Alps (CH)
Land cover classes have been derived from bands of Sentinel-2 (bands 3 (green, 10m), 4 (red, 10m), 8 (near infrared, 10m), 11 (SWIR, 20m) and 12 (SWIR, 20m)) as well as Sentinel-1 VV (IW mode) using a combined approach of unsupervised and supervised classification. The dataset comprises the classification result as well as the signature file for the Maximum Likelihood Classification. Covered areas are: Western Siberia (Russia), Barrow (Alaska), Teshekpuk (Alaska), Mackenzie Delta (Canada), Umiuaq (Canada), Kytalyk (Russia), Lena Delta (Russia), Seward peninsula (Alaska), Yukon Delta (Alaska). For more information see the product guide.
Land Cover Classification from Sentinel-1 and Sentinel-2, 2015-2018, Mackenzie Delta, Beaufort Sea Region (CA)
This dataset includes global maps of methane (CH4) emissions from inland dam-reservoir systems at 0.25-degree spatial resolution. Daily emission rates (as grams of CH4 per day per total area of grid cell) were estimated for boreal, temperate, and subtropical-tropical eco-climatic domains and total emissions. The annual duration of the emission season is based on freeze-thaw cycles of these water bodies as applicable. In addition, the dataset includes the total fractional area of reservoirs in each grid cell. These estimates will promote understanding of the current and future role of reservoirs in the global CH4 budget and guide efforts to mitigate reservoir-related CH4 emissions. These emission estimates are climatological; one daily value for each day of year (n=365) is provided for each grid cell. Modeled estimates were based on daily mean inputs, averaged over 2002 to 2015. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Johnson, M. S. et al. (2021). Spatiotemporal methane emission from global reservoirs. Journal of Geophysical Research: Biogeosciences, 126, e2021JG006305. https://doi.org/10.1029/2021JG006305 This dataset is openly shared, without restriction, in accordance with the [EOSDIS Data Use Policy](https://earthdata.nasa.gov/earth-observation-data/data-use-policy?_ga=2.88701406.419703260.1676448349-1262327661.1663154735).
Global-Gridded Daily Methane Emissions from Inland Dam-Reservoir Systems, 2002-2015
The data sets were made during the summer 2021, with samples collected from three cores, at two depths (active and permafrost layers). In total, six samples (3 replicates by samples) were incubated for 67 days at two temperatures (4°C and 20°C). Core sampling were performed during the joint Russian-German LENA 2018 expedition. The data sets were both collected at Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research and GeoForschungsZentrum Helmholtz-Zentrum, Potsdam, Germany. The aim of this study was to understand and quantify how much carbon may be lost during short-term permafrost thaw across different landscape units at the example of study area in the Lena Delta, Siberia. The study measures greenhouse gases (GHG) emissions based on an incubation experiment and focuses on relationships between GHG emissions and microbial abundance shifts during short-term permafrost thaw under anaerobic conditions. The objectives of the study were to: (1) Quantify CH4 and CO2 production during a short-term anaerobic incubation; (2) Establish relationships between CH4 and CO2 production and microbes (methanogens and methanotrophs); (3) and to identify settings and controls that drive gas production rates in thawed permafrost soils.
Anaerobic incubation measurements (CH4+CO2) and microbe quantification, Lena Delta, 2018
2011 Tower data | Must sign in to ORNL DAAC to download
Xu, J., Morris, P. J., Liu, J. and Holden, J.: PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis, CATENA, 160, 134–140, doi:https://doi.org/10.1016/j.catena.2017.09.010, 2018.
TCVIS-2003-2022 Mosaic East Cloud Optimized GeoTIFF (COG)
Thermokarst lagoons develop in permafrost lowlands along the ice-rich Arctic coast when thermokarst lakes or basins with bottom elevations at or below sea level are breached by the sea due to erosion, sea-level rise, or connection via drainage channels. Thermokarst lagoons, as dynamic landforms at the interface of terrestrial permafrost and marine systems, play a crucial role in the transformation of permafrost carbon under rising marine influence. Here we present a comprehensive dataset consisting of the first manual thermokarst lagoon area mapping, a more precise number of thermokarst lagoons and a detailed lagoon classification for thermokarst lagoons along the pan-Arctic coast from Taymyr Peninsula in Russia to the Tuktoyaktuk Peninsula in Canada.\r\n\r\nThis is an updated dataset based on the previous work of Jenrich et al. 2021 and Jenrich et al. 2023. The main improvements include (1) counting thermokarst lagoons individually within a lagoon system, as long as the distinct round form of former lake basins is visible; (2) manually calculating the area for all mapped thermokarst lagoons based on the updated Global Surface Water Dataset from 1984-2021 by Pekel et al., 2016; and (3) classifying lagoons based on connectivity to the sea into 5 connectivity classes. We identified 520 thermokarst lagoons covering an area of 3457 km2.\r\n\r\nMethods: Pan-Arctic thermokarst lagoon distribution and area were mapped using QGIS version 3.34 and Google Earth Engine. The updated Global Surface Water Dataset by Pekel et al., 2016, based on Landsat-5, -7, and -8 satellite images from 1984-2021, was used to create masks with a threshold of >75% based on water occurrence, which enabled the manual splitting of polygons from the resulting mask vector data and extraction of thermokarst lagoon areas. Mapping and area extraction also relied on Sentinel-2 imagery from 2023/07/01-2023/08/30, basemaps Google Satellite and ESRI Satellite, and the digital elevation model ArcticDEM and its hillshade HSarcticDEM (Porter et al., 2018). The thermokarst lagoon classification employed a geomorphological approach based on Sentinel-2 imagery and basemaps Google Satellite and ESRI Satellite. Connectivity classes were visually defined and attributed to thermokarst lagoons based on: 1) the size of the lagoon opening relative to its overall size, 2) whether it was directly connected or subsequent within a lagoon system, and 3) interactivity within the lagoon system. The five classes range from 5 - very high connectivity to 1 - very low connectivity: * 5 - Lagoon, always open (Lao) - Very high connectivity - Lagoon in direct exchange with the sea. * 4 - Lagoon, mostly open (Lmo) - High connectivity - Barrier islands or sand spits only slightly block exchange with the sea or subsequent lagoon which is very well connected to the primary lagoon. * 3 - Lagoon, semi-open (Lso) - Medium connectivity - Exchange limited either temporally or spatially due to barrier islands and sand spits or subsequent lagoon which is well connected to the primary lagoon. * 2 - Lagoon, limited open (Llo) - Low connectivity - Exchange very limited due to very small opening or narrow channel or subsequent lagoon which is less connected to primary lagoon due to small channel or high distance. * 1 - Lagoon, nearly-closed (Lnc) - Very low connectivity - Exchange strongly limited due to long and narrow channel, or subsequent lagoon with very limited exchange. Temporary lake characteristics are possible.
Pan-Arctic thermokarst lagoon distribution, area and classification from Landsat images, 1984 to 2021
Hugelius, G., Bockheim, J. G., Camill, P., Elberling, B., Grosse, G., Harden, J. W., Johnson, K., Jorgenson, T., Koven, C. D., Kuhry, P., Michaelson, G., Mishra, U., Palmtag, J., Ping, C.-L., O'Donnell, J., Schirrmeister, L., Schuur, E. A. G., Sheng, Y., Smith, L. C., Strauss, J., and Yu, Z.: A new data set for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region, Earth Syst. Sci. Data, 5, 393-402, https://doi.org/10.5194/essd-5-393-2013, 2013.
During Leg 1, the CTD (CTD RBR Maestro) was manually lowered in the water through an ice hole with a velocity of less than 0.3 ms-1 and an acquisition frequency of 6 Hz, yielding a vertical resolution of a few centimetres. During legs 2 to 4, the CTD (CTD RBR Concerto) was installed on a Seabird Scientific optical package frame, which was deployed with a velocity of 0.3 m s-1 and an acquisition frequency of 8 Hz. Only data from downcasts were used and poor quality profiles, that had been affected by ice-covered sensors, were removed. Atmospheric pressure observed at weather stations near the sampling locations (Aklavik, Inuvik, Shingle Point and Tuktoyaktuk) was used to tare the CTD pressure sensors. CTD profiles were smoothed and binned to a regular 0.01 m depth grid.
Hydrographic (CTD) profiles during 4 expeditions from spring to fall in 2019, Mackenzie Delta Region (CA)
WP4 Mackenzie 2019 phytoplankton pigment concentrations HPLC surface water
The dataset includes 14C measurements of CO2, CH4, DOC and POC mostly from the northern permafrost region. Some other studies are included from sites not underlained by permafrost. The dataset focuses on 14C measurements of gaseous soil emissions and waterborne ecosystem C fluxes but the database also included C forms belowground, such as soil gases and pore water DOC. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Estop-Aragonés, C., Olefeldt, D., Abbott, B. W., Chanton, J. P., Czimczik, C. I., Dean, J. F., et al. (2020). Assessing the potential for mobilization of old soil carbon after permafrost thaw: A synthesis of 14C measurements from the northern permafrost region. Global Biogeochemical Cycles, 34, e2020GB006672. https://doi.org/10.1029/2020GB006672
Radiocarbon content of CO2, CH4, DOC and particulate organic carbon from the northern permafrost region