Arctic lake ecosystems are sites of high biodiversity that play an important role in carbon cycling, yet the impacts of emerging warmer and wetter conditions on the ecology of these lakes is poorly understood, partly owing to insufficient long-term data. Using a 10-year dataset, we report on an abrupt, coherent, climate-driven transformation of Arctic lakes in Greenland, demonstrating how a season of both record heat and rainfall drove a state change in these systems. This change from “blue” to “brown” lake states altered numerous physical, chemical, and biological lake features. This dataset has been analyzed in a manuscript submitted to the Proceedings of the National Academy of Sciences (PNAS) titled: Abrupt transformation of West Greenland lakes following compound climate extremes associated with atmospheric rivers.

The purpose of this dataset is to report long-term (1985–2017) snow water equivalent (SWE) data collection in remote Arctic watershed. Researchers at the University of Alaska Fairbanks, Water and Environmental Research Center have been collecting end-of-winter SWE measurements in the Imnavait Creek watershed (2.2 square kilometers) since the year 1985. The measurements of snow depth and water equivalent were made during annual snow surveys in April, May and June. Four SWE data files are reported for the Imnavait Creek watershed: 1) individual SWE and snow depth measurements from 1985 to 1997 (snow_depth_swe_Imnavait_1985_1997.txt); 2) individual SWE and snow depth measurements from 1998 to 2017 (snow_depth_density_Imnavait_1998_2017.txt); 3) watershed averaged SWE from 1985 to 2017 (SWE_watershed_average_Imnavait_1985_2017.txt); 4) SWE measurements in May and June during ablation period from 1985 to 2013 (SWE_ablation_data_Imnavait.1985-2013.txt).

The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) produced a wealth of observational data along the drift of the R/V Polarstern in the Arctic Ocean from October 2019 to September 2020. These data can further process-level understanding and improvements in models. However, the observational records contain temporal gaps and are provided in different formats. One goal of the MOSAiC Single Column Model Working Group (MSCMWG: https://mosaic-expedition.org/science/cross-cutting_groups/) is to provide consistently-formatted, gap-filled, merged datasets representing the conditions at the MOSAiC Central Observatory (the intensively studied region within a few km of R/V Polarstern) that are suitable for driving models on this spatial domain (e.g., single column models, large eddy simulations, etc). The MSCMWG is an open group, please contact the dataset creators if you would like to contribute to future versions of these merged datasets (including new variables). This dataset contains version 1 of these merged datasets, and comprises the variables necessary to force a single column ice model (e.g., Icepack: https://zenodo.org/doi/10.5281/zenodo.1213462). The atmospheric variables are primarily derived from Met City (~66 percent (%) of record, https://doi.org/10.18739/A2PV6B83F), with temporal gaps filled by bias and advection corrected data from Atmospheric Surface Flux Stations ( https://doi.org/10.18739/A2XD0R00S, https://doi.org/10.18739/A25X25F0P, https://doi.org/10.18739/A2FF3M18K). Some residual gaps in shortwave radiation were filled with ARM ship-board radiometer data. Three different options for snowfall precipitation rate (prsn) are provided, based on in-situ observations that precipitation greatly exceeded accumulation on level ice, and accumulation rates varied on different ice types. MOSAiC_kazr_snow_MDF_20191005_20201001.nc uses 'snowfall_rate1' derived from the vertically-pointing, ka-band radar on the vessel (https://doi.org/10.5439/1853942). MOSAiC_Raphael_snow_fyi_MDF_20191005_20201001.nc and MOSAiC_Raphael_snow_syi_MDF_20191005_20201001.nc use snow accumulation measurements from manual mass balance sites (https://doi.org/10.18739/A2NK36626) to derived a pseudo-precipitation. MOSAiC_Raphael_snow_fyi_MDF_20191005_20201001.nc is based on the First Year Ice (fyi) sites. MOSAiC_Raphael_snow_syi_MDF_20191005_20201001.nc is based on the Second Year Ice (syi) sites. The other atmospheric variables for these files are identical. Oceanic variables are in MOSAiC_ocn_MDF_20191006_20200919.nc and are derived from https://doi.org/10.18739/A21J9790B. The data are netCDF files formatted according to the Merged Data File format (https://doi.org/10.5194/egusphere-2023-2413, https://gitlab.com/mdf-makers/mdf-toolkit). The code 'recipes' that were used to produce these data are available at: https://doi.org/10.5281/zenodo.10819497. If you use these datasets, please also cite the appropriate publications: Meteorological variables (excluding precipitation): Cox et al., 2023 (https://doi.org/10.1038/s41597-023-02415-5) Oceanographic variables: Schulz et al., 2023 (https://doi.org/10.31223/X5TT2W) KAZR-derived precipitation: Matrosov et al., 2022 (https://doi.org/10.1525/elementa.2021.00101) Accumulation-derived pseudo-precipitation: Raphael et al., in review. The following are known issues that will be addressed in future dataset releases: 1. Residual gaps occupy approximately 20% of the data record (see addendum) 2. Some transitions to shiprad downwelling shortwave are unreasonable abrupt 3. MDF format does not currently include a field for point-by-point data source Addendum: For atmospheric variables, below indicates the percentage sourced from each dataset (and the amount missing a.k.a NaN) Air Temperature metcity 0.661943 NaN 0.193333 asfs30 0.134910 asfs40 0.008607 asfs50 0.001207 Specific Humidity metcity 0.658890 NaN 0.196298 asfs40 0.008695 Wind Velocity metcity 0.666334 NaN 0.255003 asfs30 0.068828 asfs40 0.008630 asfs50 0.001205 Downwelling Longwave metcity 0.549417 asfs30 0.241502 NaN 0.209081 Downwelling Shortwave metcity 0.674166 NaN 0.158814 asfs30 0.140794 shipradS1 0.026226 Note that the 21 day gap from the end of Central Observatory 2 to the start of Central Observatory 3 occupies 5.8% of the record.

The Greenland ice sheet is losing mass, which can contribute to sea level rise. Firn aquifers covering between 22,000 – 90,000 km 2 have recently been discovered within the ice sheet. In summer, surface snowmelt infiltrates to depth, saturating pore space within the compacting firn. Recharge ceases when the surface temperatures cool below 0ºC in the fall. Instead of refreezing, the meltwater stays in liquid phase throughout the year because of the insulation produced by high snow accumulation rates. This liquid flows through the firn, and discharges from the aquifer, likely to crevasses at the edge of the ice sheet. Flow through the firn behaves according to Darcy’s law. Instead of permanently storing meltwater, either through refreezing or simple storage in pore space, firn aquifers allow large volumes of meltwater to discharge from the ice sheet. The fate of that meltwater and its pathways to the ocean remain unknown and require further work as some scenarios (e.g., hydrofracturing crevasses leading to basal lubrication) could play important roles in accelerating ice flow and discharge to the ocean. This dataset contains field data from a series of tests to characterize the hydraulic properties of a firn aquifer in Southeast Greenland. The aquifer and slug tests are meant for estimating the hydraulic conductivity of the firn aquifer. Aquifer tests were conducted by pumping water out of a borehole and measuring the water level change within the aquifer. Slug tests were conducted by lowering the water level in a sealed piezometer installed in the aquifer by pumping air into it and measuring the water level recovery upon venting the piezometer. The borehole dilution tests, which measured the decrease in specific conductance following injection of saltwater into a borehole over time as freshwater flows through the aquifer, are meant to measure the rate of liquid flowing through the firn aquifer.

This dataset contains information on ground ice obtained at the Jago River site in 2009 by means of permafrost coring. The dataset includes the data on cryostratigraphy, ice contents, and isotope composition of various types of ground ice.

USCGC (US Coast Guard Cutter) Healy (HLY) cruise 1803 took place from 25 October to 18 November 2018, departing from and returning to Dutch Harbor, Alaska (AK). The cruise was part of the project entitled “Monitoring the Western Arctic Boundary Current in a Warming Climate: Atmospheric Forcing and Oceanographic Response”, funded by the National Science Foundation as part of the Arctic Observing Network. The purpose of the cruise was to service the long-term mooring deployed in the Pacific Arctic Boundary Current on the continental slope of the Alaskan Beaufort Sea. In addition to recovering and re-deploying the mooring, the other primary objective of the cruise was to collect hydrographic measurements to further our understanding of the nature of the boundary current and its downstream evolution. This submission contains all shipboard CTD (Conductivity, Temperature, Depth) measurements from the HLY1803 cruise. For more information on the AON (Arctic Observing Network) program, visit: http://aon.whoi.edu/.

The Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC, 2019/2020), a year-long drift with the Arctic sea ice, has provided the scientific community with an unprecedented, multidisciplinary dataset from the Eurasian Arctic Ocean, covering from high atmosphere to deep ocean across all seasons. However, the heterogeneity of data and the superposition of spatial and temporal variability intrinsic to a drift campaign complicate the interpretation of observations. Here, we compile a quality-controlled dataset with best coverage and derived parameters, and provide daily average values of: drift speed, friction velocity, ocean temperature and salinity (full profiles and surface), mixed layer depth, mixing layer depth, heat flux over the halocline and thermocline. The presented core parameters offer valuable context for interdisciplinary research, fostering an improved understanding of the complex, coupled Arctic System. Results based on this dataset are described in Schulz, Koenig, Muilwijk et al.: "The Eurasian Arctic Ocean along the MOSAiC drift (2019-2020): An interdisciplinary perspective on properties and processes" (Elementa: Science of the Anthropocene, tentatively submitted September 2023). When using the parameter in this data set, we strongly advise to also cite the respective original data sets, linked in the description of methods here.

As the primary repository for the Arctic Program of the National Science Foundation, the Arctic Data Center accepts data from all disciplines, including social science research. The Social Science Data Portal is a subset of datasets from the Arctic Data Center repository that represent data from social science research. This portal will automatically update whenever a new dataset is uploaded to the repository that has been tagged as a social science dataset, or that has a keyword that represents a social science discipline.

The mass balance of sea ice and evolution of the sea ice thickness distribution is a key component of the Arctic system. It is controlled by thermodynamic ice growth and melt, mechanical redistribution through ridging and rafting, and transport. The SEDNA experiment was designed in a regional Lagrangian frame of reference, and tracked the evolution of a region of ice surrounding the APLIS 2007 ice camp, such that we can isolate the role of mechanical redistribution on the ice thickness distribution.

In Arctic landscapes, watershed processes are tightly linked to cold temperatures, permafrost, snow and glaciers, and strong seasonality in precipitation, storage, and runoff. Thus, a rapidly changing Arctic climate will affect watershed function and result in changes to the transport of water, sediment, and nutrients to downstream aquatic and marine ecosystems. There is increasing evidence of hydrologic intensification of the Arctic terrestrial water cycle, fueling inquiry into the hydrologic responses that integrate the varying climate and landscape units. Key to understanding these complex watershed processes is long-term hydrologic monitoring in Arctic Alaska. The goal of this project is to install, operate, and maintain hydroclimate observation stations in the Kuparuk River basin and adjacent catchments (Putuligayuk River) to obtain continuous data streams for the community of Arctic stakeholders. Imnavait Creek is a small (2.2 square kilometers) watershed located in the foothills region of Brooks Range and the headwaters of the Kuparuk River. The Kuparuk River flows north through the foothills and coastal plain of Alaska, before discharging into the Beaufort Sea. The gauging station at Imnavait Creek is approximately 0.5 kilometers south of the Dalton Highway, near MP289 (milepost). Imnavait Creek parallels the Upper Kuparuk basin and enters the Kuparuk River 12 kilometers north of the Upper Kuparuk gauging station. Flows at Imnavait creek persist throughout the summer months, but during the late winter months flow is practically non-existent. The stream is beaded, meaning that the channel connects numerous interspersed small ponds. Runoff in Imnavait Creek was measured by researchers at the University of Alaska Fairbanks Water and Environmental Research Center from 1985 to 2017. This dataset contains continuous runoff collected by researchers from University of Alaska Fairbanks from 1985 to 2017.

This project is a collaboration with Barry, CIRES, University of Colorado, Boulder (0632296). This project will develop a Cooperative Arctic Data and Information Service (CADIS) that will support the Arctic Observing Network (AON) and Study of Environmental Arctic Change (SEARCH) programs. CADIS will provide the discovery, access, and use of scientific data by providing near-real-time data delivery, a repository for data storage, a portal for the discovery, and tools to manipulate data. This system and data service will be built in a stepwise coherent manner and result in comprehensive long-term management for Arctic scientific data. CADIS will be a joint effort of the University Corporation for Atmospheric Research (UCAR), the National Snow and Ice Data Center (NSIDC), and the National Center for Atmospheric Research (NCAR). The project team will develop a new body of cyberinfrastructure by leveraging, integrating, and extending UCAR's Community Data Portal (CDP) framework and Unidata's THREDDS environment to form the CADIS system and portal. The CADIS portal will make it easier for scientists to locate, display, subset, publish, and analyze related data sets provided by a network of data providers. In the first year, a metadata plan will be completed; it will include AON projects and Long Term Observatory (LTO) projects. In the second year, the CADIS portal will be populated with metadata from the AON and LTO projects. In the third year, real-time delivery through CADIS of selected AON and IASOA (International Arctic System for Observing the Atmosphere) data will be accomplished, and tools for searching via a map interface, and a map server showing the location of selected AON or SEARCH components (where metadata are available) will be added. Also in the third year, system performance will be evaluated and documented, and a future direction charted. Guidance from the scientific and lay user communities will be key to implementing the CADIS facility. Information will be received via questionnaires, meetings, standing committees and individual queries to assess CADIS effectiveness and recommend improvements. This project is highly relevant to International Polar Year goals for developing comprehensive data management plans and creating legacy data sets. The intellectual merit lies in the stepwise development of a new cyberinfrastructure for management of Arctic scientific data. The broader impact of CADIS is that it creates a foundation for long-term access to data archives, discovery, delivery and analysis by the Arctic science community and other users.

This archive contains firn temperature data collected at five sites in the western Greenland Ice Sheet percolation zone. The sites are located along a lower elevation transect of the Expéditions Glaciologiques Internationales au Groenland (EGIG) line. The data are time series of firn temperature measured in boreholes drilled to 25 or 32 meter depth. The boreholes were drilled by firn coring methods after which the sensor strings were installed, and the holes were backfilled with fine sifted snow. These measurements are associated with additional datasets collected as part of a NSF Arctic Observing Network project and include measurements at multiple sites on the EGIG line of firn temperature and firn density/ice content.

This dataset contains infrared (~11.0 microns) Arctic satellite composite imagery. The Arctic composites were made every three hours (synoptic hour) creating a total of eight images per day. More recently, Arctic composites were created every hour for a total of 24 images per day. Most input satellite observations included in the composite were procured within 15 minutes of the top of the synoptic hour. No image is more than +/- 50 minutes from the top of the synoptic hour. Geostationary and Polar orbiting satellites used to generate the composite can include: Polar-orbiting Operational Environmental Satellites (POES) / National Oceanic and Atmospheric Administration (NOAA), Geostationary Operational Environmental Satellites (GOES) -East and -West, METEOSAT, Meteorological Satellites (MTSAT), FY-2, Kalpana-1, and Terra/Aqua.

This is part of continuous aerosol-related glaciochemical measurements of the Tunu 2013 ice cores collected from NE Greenland in 2013.

ERA5-Land (European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis) is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'. The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields.

Contains a list of Arctic communities suitable for providing context in other geospatial data visualizations. Dataset is limited to communities greater than or equal to 55 degrees north latitude, with populations greater than or equal to 10,000 as of 2022, except for Alaska communities which allow populations as small as 500. The intent of this dataset is to provide intuitive landmarks that help with interpretation of other geospatial datasets. This dataset contains minimal fields: community name, two-letter country abbreviation, latitude and longitude geometry, estimated population as of 2022, and Geonames identifier. This dataset is visualized on the Permafrost Discovery Gateway (https://arcticdata.io/catalog/portals/permafrost/Imagery-Viewer), an online scientific gateway that makes information of changing permafrost conditions throughout the Arctic available by providing access to very high resolution satellite data products and new visualization tools that will allow exploration and discovery for researchers, educators, and the public at large.

This archive contains data collected at sites located along the lower elevation portion of the Expéditions Glaciologiques Internationales au Groenland (EGIG) line, western Greenland Ice Sheet. The data include the physical stratigraphy measured in firn cores extending to depths of up to ~30 meters (m) with measurements of firn density and refrozen ice content. The data were collected as part of a project funded by the U.S. National Science Foundation. Each of the study sites is in the Greenland Ice Sheet’s Percolation Zone. The data were collected during May-June of 2018 and 2019. This dataset will be associated with additional datasets collected as part of a NSF Arctic Observing Network project, including physical stratigraphy and density measured in ice cores and firn temperature time series measured in core holes, as they become available in future years.

The Toolik Field Station (TFS) plant phenology program monitors the timing of specific phenological developmental stages of plant species commonly found in the moist acidic tundra plant community. The TFS phenology program began in response to TFS research community requests to collect baseline environmental data that would be broadly applicable and provide context to research projects conducted near TFS. The TFS plant phenology data collection protocol is based on the International Tundra Experiment (ITEX, www.geog.ubc.ca/itex) protocol for the Toolik Snowfence Experiment. This moist acidic tundra dataset began in 2007 and continues through 2024.

Data are available for download at http://arcticdata.io/data/10.18739/A28G8FK10 To download all files in the command line, run the following command in a terminal: wget -r -np -nH --cut-dirs=3 -R '\?C=' -R robots.txt https://arcticdata.io/data/10.18739/A28G8FK10 To download only a subdirectory of the archived files, add the subdirectory to the end of the URL above. Lake area for permanent and seasonal in the Arctic were derived from Landsat satellite imagery from 1984-2021 using machine learning. Data was collected for lakes with a maximum extent larger than one hectare, buffered by 30 meters, within four discontiguous transects across the Arctic region at latitudes greater than 60 degrees North. This region covers large parts of the northern permafrost terrain. This annual lake area data was merged with lake size trend attributes derived from the years 2011-2020. Lake area change rates were normalized by area and/or perimeter. The lake extraction methodology is based on Nitze et al, 2017 and 2018. This dataset is version 2.0. You can find version 1.0, related to Nitze et al. 2018 here: https://apgc.awi.de/dataset/hot-t1-prd-lake-ls-1999-2014 https://apgc.awi.de/dataset/hot-t2-prd-lake-ls-1999-2014 https://apgc.awi.de/dataset/hot-t3-prd-lake-ls-1999-2014 https://apgc.awi.de/dataset/hot-t4-prd-lake-ls-1999-2014 Data files were merged, and the annual permanent and seasonal water data for 2017-2021, a subset of the available years, was fed into the Permafrost Discovery Gateway visualization workflow. This workflow cleaned, standardized, and visualized the data and output two Tile Matrix Sets per year. One Tile Matrix Set is the data in the form of GeoPackages, or staged tiles, and the other Tile Matrix Set is the staged tiles in the form of GeoTIFF tiles. The highest resolution tiles were resampled to produce GeoTIFFs for lower resolutions. In the future, this dataset will be expanded for annual data for 1984-2021. This visualized data will be published on the Permafrost Discovery Gateway portal: https://arcticdata.io/catalog/portals/permafrost/Imagery-Viewer Data limitations in use: This data is part of an initial release of the pan-Arctic data product for lake area and size trends, and it is expected that there are constraints on its accuracy and completeness. Users are encouraged to provide feedback regarding how they use this data and issues they encounter during post-processing. Please reach out to the dataset contact or a member of the Permafrost Discovery Gateway team via support@arcticdata.io.

This dataset contains various declassified military satellite imagery acquired near Barrow Alaska during August, 1988, June 1989, and August 1995. Each scene is packaged into a .tar.gz file which includes metadata. Mosaics of the images are also included.