Find research datasets worth reusing

openCC0May 2025View details →

Sea ice thickness, snow depth, and sea ice freeboard in lagoon sites along the Alaska Beaufort Sea coast, 2019-ongoing

Physical parameters related to snow and sea ice have implications for lagoon circulation, sea-air heat exchange, and underwater light regimes. To understand these relationships and their greater effect on ecosystem function, the Beaufort Lagoon Ecosystem LTER (BLE LTER) uses in situ methods to assess snow depth, ice freeboard, and ice thickness in select water bodies across the Beaufort Sea coast (Elson Lagoon, Simpson Lagoon, Kaktovik Lagoon, Jago Lagoon, and Stefansson Sound). Sea ice thickness is the distance from sea ice bottom to top, not including snow. Freeboard, determined in the same drilled hole, is the distance from the surface of the water to the top of the ice, not including snow cover. These measurements are made annually, close to maximum ice thickness (typically April).

openCC0Oct 2025View details →

Ice, water, and sediment pigment concentrations from Beaufort Sea lagoons core program stations, 2023-24

Bottom ice (< 20 cm), water column, and undisturbed surface sediment samples from the Beaufort Lagoon Ecosystem Long Term Ecological Research programs were collected, in tandem, from core program sites in ice-cover (~April), ice break-up (~June), and open water (~August) seasons of 2023, and ice-cover 2024, to quantify algal pigment concentrations and variations in an annual cycle. We also ran historical samples from 2021 sampling seasons. This data can be used with analysis programs such as CHEMTAX or PhytoClass to elucidate microalgal community structure. Fourteen pigments were measured, including chlorophyll a, fucoxanthin, zeaxanthin, alloxanthin, peridinin, prasinoxanthin, lutein, chlorophyll c3, 19-hexanoyloxyfucoxanthin, and 19-butanoyloxyfucoxanthin. Phaeopigments (pheophytin, pheophorbide, and chlorophyllide a) were also included in these analyses. For sediment samples, the values of chlorophyll a, fucoxanthin, zeaxanthin, alloxanthin, peridinin, pheophytin, pheophorbide, and chlorophyllide a can be found in the core program pigment dataset, which is a continuously collected data set (<a href="https://doi.org/10.6073/pasta/5294f45c9c7287903078926a487f1fd7" style="text-decoration: underline;">Sediment pigment concentrations</a>). Pigment concentrations were measured using high-precision liquid chromatography (HPLC). Concentrations are represented as μg L-1 for both ice and water column samples, and as μg g-1 for sediment samples.

openCC0Dec 2023View details →

Influence of Little Ice Age on New England Vegetation from 2000 BP to Present

This multi-proxy study uses paleoecological, paleolimnological, and historical approaches to reconstruct climate, vegetation, and cultural dynamics over the past 1500 years at sites arrayed across the climatic and forest gradients of New England and to place these results in a regional framework through analysis of pollen records from the North American Pollen Database. High resolution records were obtained using pollen to interpret vegetation history; chironomids, stable isotopes, geochemistry, and diatoms, supplemented by historical reconstructions, to interpret climate history; charcoal and land-use data to document the human impacts; and Pb-210 and C-14 for chronological control. Results will provide: (1) an objective characterization of the Little Ice Age and climate history in New England, (2) comparison of pre- and post-European forest dynamics in relationship to independent environmental and land-use histories, (3) a reexamination of historical vegetation dynamics in light of prior climate an vegetation change, and (4) widespread availability of data and results through publications, symposium presentation, and the Harvard Forest Archives and web pages.

North Temperate Lakes LTER Long-term winter chemical limnology and days since ice-on for primary study lakes 1982 - 2014

This data set integrates long-term data sets on winter nutrient chemistry with ice phenology (number of days since ice-on), focusing on the subset of measurements taken during ice cover. Parameters characterizing limnology of 5 primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, and Trout lakes, are measured at one station in the deepest part of each lake at the surface, middle, and deep (~1 meter above bottom). These parameters include nitrate-N, ammonium-N, total dissolved phosphorus, dissolved inorganic carbon, water temperature, dissolved oxygen, and pH. Water temperature and dissolved oxygen values are the zonal averages from more complete depth profiles. Sampling Frequency: every 6 weeks during ice-covered season for the northern lakes. Number of sites: 5

Lake snow removal experiment zooplankton community data, under ice, 2019-2021

Although it is a historically understudied season, winter is now recognized as a time of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging research points to a future of reduced ice cover duration and changing snow conditions that will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice conditions, and subsequent changes to under-ice light environment, might impact ecosystem dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019 season served as a reference year, and snow was not removed from the lake. This dataset represents under ice zooplankton community samples (integrated tows at depths of 7 m) and some shoulder-season (open water) zooplankton community samples. Zooplankton samples were preserved in 90% ethanol and later processed to determine taxonomic classification at the species-level, density (individuals / L), and average length (mm).

openCC (other)Sep 2025View details →

Lake ice clearance and formation data for Green Lakes Valley, 1968 - ongoing.

Records were based on intermittent observation of the extent of ice cover on Silver Lake, Lake Albion, and Green Lakes 1-5, dependent on observers present for other reasons (or in some cases from photos or satellite data). Dates of freeze-up (formation), breakup (first melt or open water), and lake-ice clearance were recorded, along with visual estimates of percent ice cover at various dates prior to complete meltout in spring. The latter estimates are only included starting in 2019.

zenodo56/100

Sea-ice induced Southern Ocean subsurface warming and surface cooling in a warming climate: ROMS model data

Abstract: This data set contains model output data from the regional ocean modelling system (ROMS) that was set up for the Southern Ocean (south of 24 °S; Haumann, 2016) to analyze the effects of changing surface freshwater and momentum fluxes on Southern Ocean water-mass changes over the period 1980 to 2011. All data is provided in NetCDF format. The data set contains 3 sets 40-year long model spin-up, control, and 3 perturbation simulations each. The sets differ by their model mean state to assess the effect of surface salinity biases on the results. One set of simulations are reference simulations where salinity is restored to the observed surface salinity during model spin-up, and the other two sets are simulations in which the restoring surface salinity has been altered by plus and minus 0.1 PSU, respectively. The control and perturbation simulations are 40-year extensions of the respective model spin-up simulations. In the perturbation simulations either the surface freshwater fluxes or momentum fluxes are instantaneously perturbed after the spin-up simulation to reflect the observation-derived changes in these surface fluxes and then held constant (at the perturbed level) for 40 years. They consist of sea-ice freshwater flux, glacial meltwater flux, and atmosphere-ocean momentum flux changes. Details on the model setup, forcing, and simulations can be obtained from the related research article by Haumann et al. (2020; https://doi.org/10.1029/2019AV000132). Contacts: F. Alexander Haumann, ORCID: 0000-0002-8218-977X. Email: alexander.haumann@gmail.com License: When using this data users must cite the original research article by Haumann et al. (2020) published in AGU Advances (https://doi.org/10.1029/2019AV000132). This model output data set is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/ Citations: Haumann, F. A., Gruber, N., Münnich, M. (2020): Sea-ice Induced Southern Ocean Subsurface Warming and Surface Cooling in a Warming Climate. AGU Advances, 1, e2019AV000132. https://doi.org/10.1029/2019AV000132 Haumann, F. A., Gruber, N., Münnich, M. (2020): Sea-ice induced Southern Ocean subsurface warming and surface cooling in a warming climate: ROMS model data. (Version 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3709154 Data processing: The model data provided here has been processed for analysis and, due to storage constraints, only variables and averages used in the related research article (Haumann et al., 2020) are published here. Years refer to model years since initiation, i.e. the start of the spin-up simulation, and reach a maximum of 80 at the end of the control and perturbation experiments. Averages are either annual means over an indicated period ('yearly'), overall means over an indicated period ('mean'), or monthly climatologies over an indicated period ('clim'). 'Yearly' and 'Clim' values are provided either for surface fields or vertically integrated fields ('surf'). 'Mean' values are provided on the native vertical model grid as 3D fields. 'Yearly' zonal mean ('zonalmean') values have been first interpolated to from the native vertical model grid to a regular vertical z-level grid and then zonally averaged. All data has been stored in NetCDF format and compressed using level 1 deflation (https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression). Full monthly mean model output from these simulations and the forcing fields can be obtained from the corresponding author upon request (alexander.haumann@gmail.com). Standards: Data files have been formatted as Network Common Data Form (NetCDF; https://www.unidata.ucar.edu/software/netcdf/) in Hierarchical Data Format, version 5 (https://portal.hdfgroup.org/display/knowledge/HDF5+Documentation) Contents: Data - ROMS_SO_d025_grd.nc, ROMS grid file (Note: one row of ghost points on either side of the grid), compressed netcdf-file - ROMS_SO_d025_ref_spinup, ROMS output from reference spin-up simulation, 4 netcdf-files:   -- ROMS_SO_d025_ref_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_ref_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_ref_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_ref_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file - ROMS_SO_d025_minus01_spinup, ROMS output from minus 0.1 PSU sensitivity spin-up simulation, 4 netcdf-files:   -- ROMS_SO_d025_minus01_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_minus01_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_minus01_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_minus01_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file - ROMS_SO_d025_plus01_spinup, ROMS output from plus 0.1 PSU sensitivity spin-up simulation, 4 netcdf-files:   -- ROMS_SO_d025_plus01_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_plus01_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_plus01_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_plus01_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file - ROMS_SO_d025_ref_ctrl, ROMS output from reference control simulation, 4 netcdf-files:   -- ROMS_SO_d025_ref_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_ref_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_minus01_ctrl, ROMS output from minus 0.1 PSU sensitivity control simulation, 4 netcdf-files:   -- ROMS_SO_d025_minus01_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_minus01_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_plus01_ctrl, ROMS output from plus 0.1 PSU sensitivity control simulation, 4 netcdf-files:   -- ROMS_SO_d025_plus01_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_plus01_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_ref_seaice, ROMS output from reference sea-ice freshwater flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_ref_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_ref_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_minus01_seaice, ROMS output from minus 0.1 PSU sensitivity sea-ice freshwater flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_minus01_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_minus01_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_plus01_seaice, ROMS output from plus 0.1 PSU sensitivity sea-ice freshwater flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_plus01_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_plus01_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_ref_glacial, ROMS output from reference glacial meltwater flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_ref_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_ref_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_minus01_glacial, ROMS output from minus 0.1 PSU sensitivity glacial meltwater flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_minus01_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_minus01_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_plus01_glacial, ROMS output from plus 0.1 PSU sensitivity glacial meltwater flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_plus01_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_plus01_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_ref_momentum, ROMS output from reference atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_ref_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_ref_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_minus01_momentum, ROMS output from minus 0.1 PSU sensitivity atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_minus01_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_minus01_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_plus01_momentum, ROMS output from plus 0.1 PSU sensitivity atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_plus01_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_plus01_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file   File headers - ROMS_SO_d025_grd:     angle [radians]: angle between xi axis and east     f [second-1]: Coriolis parameter at RHO-points     h [meter]: Final bathymetry at RHO-points     hraw [meter]: Working bathymetry at RHO-points     lat_rho [degree_north]: latitude of RHO-points     lat_psi [degree_north]: latitude of PSI-points     lat_u [degree_north]: latitude of U-points     lat_v [degree_north]: latitude of V-points     lon_rho [degree_east]: longitude of RHO-points     lon_psi [degree_east]: longitude of PSI-points     lon_u [degree_east]: longitude of U-points     lon_v [degree_east]: longitude of V-points     mask_rho [-]: mask on RHO-points     mask_u [-]: mask on U-points     mask_v [-]: mask on V-points     pm [meter-1]: curvilinear coordinate metric in XI     pn [meter-1]: curvilinear coordinate metric in ETA     spherical [char]: Grid type logical switch - ROMS_SO_d025_*_clim.*.surf.nc, ROMS_SO_d025_*_yearly.*.surf.nc:     time [days since 0000-01-01 00:00:00]: time     lon [degree_east]: longitude     lat [degree_north]: latitude     zeta [meter]: averaged free-surface elevation     temp [degree_celsius]: averaged potential temperature     salt [psu]: averaged salinity     rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3     mld [meter]: averaged mixed layer depth     n2 [seconds-2]: averaged buoyancy frequency (upper 100 m)     n2temp [seconds-2]: averaged buoyancy frequency due to temperature (upper 100 m)     n2salt [seconds-2]: averaged buoyancy frequency due to salinity (upper 100 m)     heat_100 [10^18 J]: averaged ocean heat content (upper 100 m)     heat_100_2000 [10^18 J]: averaged ocean heat content (between 100 m and 2000 m)" ; - ROMS_SO_d025_*_mean.*.nc:     time [days since 0000-01-01 00:00:00]: time     lon [degree_east]: longitude     lat [degree_north]: latitude     zeta [meter]: averaged free-surface elevation     temp [degree_celsius]: averaged potential temperature     salt [psu]: averaged salinity     rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3 - ROMS_SO_d025_*_yearly.zlev.zonalmean.*.nc:     time [days since 0000-01-01 00:00:00]: time     lat [degree_north]: latitude of RHO-points     lat_v [degree_north]: latitude of V-points     depth [meter]: depth of levels     temp [degree_celsius]: averaged potential temperature     salt [psu]: averaged salinity     rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3     moc [10^6 meter^3 second^-1]: averaged meridional overturning circulation on V-points Missing values: missing values are filled as '-9.99e+20'. Metadata - README.txt, metadata, text format References: Haumann, F. A. (2016): Southern Ocean response to recent changes in surface freshwater fluxes. Doctoral Thesis. ETH Zurich. doi:10.3929/ethz-b-000166276. Acknowledgments: This work was supported by ETH Research Grant CH2-01 11-1 and by the SNSF grant numbers P2EZP2_175162 and P400P2_186681, as well as NSF’s SOCCOM Project under NSF Award No. PLR-1425989.

opencc-by-4.0Dec 2019View details →

openCC (other)Mar 2022View details →

Meteorological data collected on Toolik Lake during the ice free season for 2014-2020, Arctic LTER, Toolik Research Station, Alaska

File describing the meteorological conditions on Toolik Lake (named the Toolik Lake Climate station), adjacent to the Toolik Field Research Station (68 38'N, 149 36'W). This is a floating climate station and should not be confused with the Toolik Field Station Climate site (TFS Climate Station or Met Station) which is a terrestrial station (located on land). Note that this land station has been called the "Toolik Main Climate Station", and the station on the lake is located where the main lake sampling site is located so it has also been called the Toolik Lake Main Climate Station. Measurements include air temperature, relative humidity, wind speed and direction, and radiation. Note: There are no discharge data for 2013 because of equipment malfunction.

openCC0Mar 2021View details →

Seasonal Ice Mass-balance Buoy (SIMB) measurements from sites along the Beaufort Sea Coast, Alaska, 2018-ongoing

Measurements of the thickness of sea ice and the depth of its snow cover allow us to calculate how their mass changes in response to the varying fluxes of heat between the ocean and atmosphere over the course of a season. Repeated drill measurements are not ideal for this purpose since each drill hole disturbs the ice and its insulating snow cover. Also, spatial variability in ice thickness can mask temporal changes if holes are not drilled in the same place each time. Hence, methods that do not require re-drilling are preferred. Automated systems such as the Seasonal Ice Mass-balance Buoy (SIMB; Planck et al, 2019) provide high temporal resolution for capturing sub-daily variations and typically include sensor strings to measure the vertical temperature profile from the air to the ocean, which can be used to infer other properties of the ice cover such as strength and porosity. Under the Beaufort Lagoon Ecosystems LTER (BLE LTER) research program, several SIMBs are deployed at sites along the Beaufort Sea coast and record a suite of parameters including but not limited to snow depth, ice thickness, position of ice surface and bottom, water/air temperature, and vertical profiles of temperature. Planck, C. J., J. Whitlock, C. Polashenski, and D. Perovich (2019), The evolution of the seasonal ice mass balance buoy, Cold Regions Science and Technology, 165, 102792, doi: https://doi.org/10.1016/j.coldregions.2019.102792.

Little Rock Lake Experiment at North Temperate Lakes LTER: Snow and Ice Depth 1984 - 2000

The Little Rock Acidification Experiment was a joint project involving the USEPA (Duluth Lab), University of Minnesota-Twin Cities, University of Wisconsin-Superior, University of Wisconsin-Madison, and the Wisconsin Department of Natural Resources. Little Rock Lake is a bi-lobed lake in Vilas County, Wisconsin, USA. In 1983 the lake was divided in half by an impermeable curtain and from 1984-1989 the northern basin of the lake was acidified with sulfuric acid in three two-year stages. The target pHs for 1984-5, 1986-7, and 1988-9 were 5.7, 5.2, and 4.7, respectively. Starting in 1990 the lake was allowed to recover naturally with the curtain still in place. Data were collected through 2000. The main objective was to understand the population, community, and ecosystem responses to whole-lake acidification. Funding for this project was provided by the USEPA and NSF. Snow and ice depth are measured during the winter months on the reference and treatment basins of Little Rock Lake. Sampling Frequency: varies - Number of sites: 4

Lake Mendota at North Temperate Lakes LTER: Snow and Ice Depth 2009-2010

Ice core data collected by Yi-Fang (Yvonne) Hsieh and collaborators for her PhD project, “Modeling Ice Cover and Water Temperature of Lake Mendota.” Part of the project was the development of a 3D hydrodynamic-ice model that simulated both temporal and spatial distributions of ice cover on Lake Mendota for the winter 2009-2010. The parameters from these ice core data were used as model inputs to run model simulations. Parameters measured include: blue ice, white ice, snow depth, and total ice. On February 13, 2009, ice cores were taken on Lake Mendota at four different stations. From January 14, 2010 through March 3, 2010 ice cores were taken on Lake Mendota at 31 different stations. In addition, ice cores were taken on other Yahara Lakes during February of 2009: Lake Kegonsa (4 stations_February 6), Lake Waubesa (4 stations_February 7), Lake Wingra (2 stations_February 8), and Lake Monona (4 stations_February 8). Only total ice measurements are reported for 2009. Included in this data set are the ice core data, and geospatial information for ice coring stations. Documentation: Hsieh, Y.-F., 2012a. Modeling ice cover and water temperature of Lake Mendota. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 157.

High Frequency Under-Ice Water Temperature Buoy Data - Crystal Bog, Trout Bog, and Lake Mendota, Wisconsin, USA 2016-2020

Water temperature measurements from three Wisonsin lakes. Two bog lakes are in Northern Wisconsin, Lake Mendota is in Southern Wisconsin. Thermistor chains span the full depth of each lake. See freeze dates for periods of open or frozen lake (NTL 32, DOI 10.6073/pasta/1c1acdb5489a0355f6f8bb5c496fdf8b and NTL 33, DOI 10.6073/pasta/22a5b5f8bce193353e559918b0024f9d)

opencc-by-4.0Jul 2020View details →

Dataset: Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean

This dataset is linked to this manuscript entitled "Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean" published in Elementa: Science of the Anthropocene (<a href="http://doi.org/10.1525/elementa.430">http://doi.org/10.1525/elementa.430</a>). Please find the abstract below: The decline of sea-ice thickness, area, and volume due to the transition from multi-year to first-year sea ice improves the under-ice light environment for pelagic Arctic ecosystems. One unexpected and direct consequence of this transition, the proliferation of under-ice phytoplankton blooms (UIBs), challenges the paradigm that waters beneath the ice pack harbor little planktonic life. Little is known about the diversity and spatial distribution of UIBs in the Arctic Ocean, or the environmental drivers behind their timing, magnitude, and species composition. Here, we compiled a unique and comprehensive dataset from seven major research projects in the Arctic Ocean (11 expeditions, covering the spring sea-ice-covered period to summer ice-free conditions) to identify the environmental drivers responsible for initiating and shaping the magnitude and assemblage structure of UIBs. The temporal dynamics behind UIB formation related to the ways that snow and sea-ice conditions impact the under-ice light field. In particular, the onset of snowmelt significantly increased under-ice light availability (> 0.1–0.2 mol photons m–2 d–1), marking the concomitant termination of the sea-ice algal bloom and initiation of UIBs. At the pan-Arctic scale, bloom magnitude (expressed as maximum chlorophyll a concentration) was predicted best by winter water Si(OH)4 and PO43– concentrations, as well as Si(OH)4:NO3– and PO43–:NO3– drawdown ratios, but not NO3– concentration. Two main phytoplankton assemblages dominated UIBs (diatoms or Phaeocystis), driven primarily by the winter nitrate:silicate (NO3–:Si(OH)4) ratio and the under-ice light climate. Phaeocystis co-dominated in low Si(OH)4 (i.e., NO3:Si(OH)4 molar ratios > 1) waters, while diatoms contributed the bulk of UIB biomass when Si(OH)4 was high (i.e., NO3:Si(OH)4 molar ratios < 1). The implications of such differences in UIB composition could have important ramifications for Arctic biogeochemical cycles, and ultimately impact carbon flow to higher trophic levels and the deep ocean.

opencc-by-4.0Jun 2022View details →

Earliest snowmelt estimation dates for Arctic sea ice (2003)

Earliest snowmelt estimation dates calculated for the year 2003 are provided using sea ice brightness temperatures from AMSR-E (Cavalieri et al., 2014) and DMSP SSM/I-SSMIS (Meier et al., 2019), as well as simulated sea ice brightness temperatures from the CESM2 JRA-55 (Danabasoglu et al., 2020; Kobayashi et al., 2015; Tsujino et al., 2018), which were created using the Arctic Ocean Observation Operator (ARC3O; Burgard et al, 2020a,b).  Scripts and README files are provided for preparing the model data to act as input to ARC3O.

opencc-by-4.0May 2024View details →

Ice sheet surface elevation change from ablation stake measurements on bare ice in the western Greenland ablation zone during July 2016

Measurements of ice surface elevation change from a network of twelve bamboo ablation stakes installed in the western Greenland ice sheet ablation zone (67.0496o N, 49.0201o W, 1215 m a.s.l.). Stakes were installed by drilling 3 m deep holes into the ice, inserting the bamboo stakes, and allowing them to freeze into the ice for 24 hours. Following the 24 hour freeze-in period, measurements of the distance from the top of the stake to its base were recorded at nominal 3 hour intervals continuously from 12:00 local time (UTC-2) on 6 July 2016 to 23:00 local time on 12 July 2016. Prior to each measurement, a 24×24 cm square wooden ablation board was placed at the base of the stake and oriented to true north. This board operated as a datum from which the stake height above the ice surface was measured.

opencc-by-4.0Jul 2024View details →

Arctic sea ice velocity in summer from AMSR2 (2013-2023)

Sea ice drift in summer plays a key role in Arctic sea ice mass balance and navigation safety of the Arctic Passage. Resulted from surface melt over sea ice and atmospheric water vapor, previous passive microwave sea ice velocity data present relatively poor quality in summer than in winter. Here, based on an improved sea ice velocity retrieval method, we produced daily Arctic sea ice velocity data during summertime (May 1st to September 30th) from 2013 to 2023. These sea ice velocity data are derived from the daily gridded AMSR2 brightness temperature (TB) at 36.5 GHz channel distributed by the University of Bremen using the continuous maximum cross-correlation algorithm. We used the polarization difference of TB to track the displacement of the sea ice templates. The size of templates is 11×11 pixels, and the spatial spacing between adjacent templates is five pixels. The time interval of this data is 24 h, and the spatial resolution is 62.5 km. Outliers were identified and discarded by surface wind (10-m wind derived from ERA5 atmospheric reanalysis) and surrounding sea ice velocity vectors. Vectors over open water areas were discarded by sea ice concentration with 6.25 km distributed by the University of Bremen.

opencc-by-4.0Jul 2024View details →

Antarctic Ecosystem Inventory: Spatial data for Ice-free lands v1.0

This is Antarctica’s first comprehensive ecosystem map of ice-free lands. The data comprise a spatially explicit 3-tiered hierarchical ecosystem classification with nine Major Environment Types (tier 1), 33 Habitat Complexes (tier 2) and 269 Bioregional Ecosystem Types (tier 3). These Bioregional Ecosystem Types are aligned with ‘level 4’ of the IUCN Global Ecosystem Typology (Keith et al. 2022).  The spatial data are available in raster format (TIF) at 100 m resolution in the Polar Stereographic Projected Coordinate System (GCS_WGS_1984) for all known ice-free areas south from latitude -57.330551 decimal degrees South (pdf map shows extent of ice-free areas in relation to terrestrial ice and ice shelves). A value attribute table (VAT) provides text fields containing codes and full names for each unit in each level of the classification hierarchy and the spatial extent of tier 3 units in hectares. Methods of development, source data and uses of the inventory are detailed by Tóth et al. (2025a). Descriptive profiles for tier 1 and 2 units are available in Tóth et al. (2025b). References Keith, D.A., Ferrer-Paris, J.R., Nicholson, E., Bishop, M.J., Polidoro, B.A., Ramirez-Llodra, E., Tozer, M.G., Nel, J.L., Nally, R. Mac, Gregr, E.J., Watermeyer, K.E., Essl, F., Faber-Langendoen, D., Franklin, J., Lehmann, C.E.R., Etter, A., Roux, D.J., Stark, J.S., Rowland, J.A., Brummitt, N.A., Fernandez-Arcaya, U.C., Suthers, I.M., Wiser, S.K., Donohue, I., Jackson, L.J., Pennington, R.T., Iliffe, T.M., Gerovasileiou, V., Giller, P., Robson, B.J., Pettorelli, N., Andrade, A., Lindgaard, A., Tahvanainen, T., Terauds, A., Chadwick, M.A., Murray, N.J., Moat, J., Pliscoff, P., Zager, I. & Kingsford, R.T. (2022) A function-based typology for Earth’s ecosystems. Nature 610, 513–518. [doi: 10.1038/s41586-022-05318-4]. Tóth, A.B., Terauds, A., Chown, S.L., Hughes, K.A., Convey, P., Hodgson, D.A., Cowan, D.A., Gibson, J., Leihy, R.I., Murray, N.J., Robinson, S.A., Shaw, J.D., Stark, J.S., Stevens, M.I., van den Hoff, J., Wasley, J. and Keith D.A. (2025a). A dataset of Antarctic ecosystems in ice-free lands: classification, descriptions, and maps. Scientific Data 12, 133. [https://doi.org/10.1038/s41597-025-04424-y]  Tóth, A.B., Terauds, A., Chown, S.L., Hughes, K.A., Convey, P., Hodgson, D.A., Cowan, D.A., Gibson, J., Leihy, R.I., Murray, N.J., Robinson, S.A., Shaw, J.D., Stark, J.S., Stevens, M.I., van den Hoff, J., Wasley, J. & Keith D.A. (2025b). Antarctic Ecosystem Inventory: Descriptive profiles for ice-free lands v1.0. DOI: 110.5281/zenodo.14625890. Australian Antarctic Data Centre.