Find research datasets worth reusing

Manual in-situ measurements of snow depth and snow water equivalent at the Polish Polar Station Hornsund - winter seasons 2021/2022and 2022/2023

The dataset presents manual measurements of snow depth and snow water equivalent collected at the Polish Polar Station Hornsund in Svalbard during the winter seasons of 2021/2022 and 2022/2023. Snow depth measurements have been conducted at the same location by the Station's overwintering personnel since August 1982. Snow depth is calculated from a mean of three snow stakes to avoid the effects of the drifting snow. Measurements are taken manualy, on a daily basis.  Snow water equivalent measurements have also been carried out at the same points by the Station's overwintering crew since October 1982. These measurements are performed every five days using a VS-43 snow tube. However, measurements are not taken when the snow depth is less than 5 cm.

NASA Eulerian Snow On Sea Ice Model Version 1.1 (NESOSIMv1.1) data: 1980 - 2024

Repository updates Update on Sep 12th 2024: The repository now includes NESOSIM v1.1 output from September 1st 2022 to April 30th 2023 and September 1st 2023 to April 30th 2024  Update on Sep 5th 2022: The repository now includes NESOSIM v1.1 output from September 1st 2021 to April 30th 2022 Update on June 7th 2022: The repository now includes NESOSIM v1.1 output from September 1st 2021 to March 31st 2022 Update on March 8th 2021: The gridded forcing files are now available in the gridded_forcings.zip file. Data are stored as Python pickles which can be easily read in by the core NESOSIM source code.  Update on March 8th 2021: The repository now includes zip files of gridded forcing (snowfall, winds, ice drift, ice concentration, initial conditions) as well as gridded Operation IceBridge snow depths.  Update on January 30th 2021: The repository now also includes a NESOSIM v1.1. daily gridded snow climatology using the mean (np.nanmean) of all data available between September 1 2010 and April 30 2020. Overview NESOSIM is a three-dimensional, two-layer (vertical), Eulerian snow on sea ice budget model developed with the primary aim of producing daily estimates of the depth and density of snow on sea ice across the polar oceans through the winter accumulation season, generally September through April (Petty et al., 2018). This repository contains model output from September 1st 1980 to April 30th 2021 [and September 1st 2021 to March 31st 2022 as of June 7th 2022] based on the NESOSIM v1.1 code release which is available on GitHub (https://github.com/akpetty/NESOSIM/tree/v1.1) and archived through Zenodo (10.5281/zenodo.4448355). More information about changes between the v1.0 and v1.1 model framework can be found in those links. A preprint is now available in The Cryosphere Discuss explaining these upgrades and their impacts on ICESat-2 winter Arctic sea ice thickness estimates (Petty et al., 2022).  Data production: Data are re-initialized at the end of summer each year (September 1st) using summer near-surface air temperature-scaled initial snow depths and run through until the end of April of the following year. The 1987-1988 winter is missing due to the lack of passive-microwave derived ice concentration data available during this period. Daily data are generated on a 100 km x 100 km North Polar Stereographic grid (EPSG: 3413) across the entire Arctic Ocean including the peripheral seas. Forcings: NB: Recent year runs often require the use of near-real-time data products, so the underlying forcings used in this v1.1 release can change in time, as noted below: <ul> <li>Snowfall: European Center for Medium Range Weather Forecasts (ECMWF) ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards2) + CloudSat scaling (Cabaj et al., 2020).</li> <li>Near-surface winds: ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> <li>Sea ice drift: NSIDC Polar Pathfinder v4 (https://nsidc.org/data/nsidc-0116, September 1 1980 to April 30 2019), OSI SAF merged (https://osi-saf.eumetsat.int/products/osi-405-c, September 1 2019 onwards).</li> <li>Sea ice concentration: Final v3 NSIDC Climate Data Record (https://nsidc.org/data/g02202/versions/3/, September 1 1980 to December 31 2020), and near-real-time v2 NSIDC Climate Data Record (https://nsidc.org/data/g10016, January 1 2021 onwards).</li> <li>Near-surface air temperature (to derive temperature-scaled initial conditions): ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> </ul> The forcings used to generate each winter dataset are described in a new 'forcings' variable in each NetCDF file.  Operation IceBridge snow depths: The repository now also includes the gridded Operation IceBridge snow depths we used for calibration purposes, as described in Petty et al., (2022). The data contained within gridded_oib_snowdepths.zip includes the daily gridded data on the NESOSIM v1.1 100 km domain, ordered by day of collection. Data are stored as Python pickles and text files and include estimates derived from the following snow depth algorithms: SRLD (2009-2015): snow radar layer detection, JPL (2009-2015): Jet Propulsion Laboratory, GSFC (2009-2015): Goddard Space Flight Center, NSIDC (2009-2012): archived NASA GSFC data on the NSIDC, QL (2013-2019): NSIDC quick-look data based on the GSFC algorithm. MEDIAN (2010-2015): consensus snow depth from median of GSFC, JPL and SRLD.  References: Cabaj, A., P. J. Kushner, C. G. Fletcher, S. Howell, A. Petty (2020), Constraining reanalysis snowfall over the Arctic Ocean using CloudSat observations, Geophysical Research Letters, 47, doi:10.1029/2019GL086426. Petty, A. A., M. Webster, L. N. Boisvert, T. Markus (2018), The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: Initial model development and analysis, Geosci. Model Dev., doi: 10.5194/gmd-11-4577-2018. Petty A. A., N. Keeney, A. Cabaj, P. Kushner, M. Bagnardi (2023), Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection, The Cryosphere, 17, 127–156, doi: 10.5194/tc-17-127-2023.

opencc-by-4.0Aug 2024View details →

Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"

This datasset contains data to reproduce the following figures of the paper Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site: <ul> <li> Time series data of Figures 1c and 2 </li> <li> Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4) </li> <li>Pléiades snow depth map (Figure S1)</li> <li> Data used for plotting Figure S2 </li> </ul>

University of Tromso Arctic Ocean freeboard and snow depth product from CryoSat-2, AltiKa and ICESat-2

Dual-frequency snow depth estimates for the Arctic Ocean in Oct-Apr 2018-2023 derived from gridded 25-km resolution CryoSat-2 and SARAL AltiKa radar freeboards and ICESat-2 laser freeboards. Waveform modelling approach applied to radar altimeters, ICESat-2 laser altimetry freeboards from ATL20 r004. See acompanying publication in The Cryosphere for further details.

Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output with snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC

The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 with snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight. There is also an upload with simulation output for the same night, but without snow cover (Rauchöcker et al., 2024a). Also available in a different dataset are data from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period. <h3>WRF Simulation Output</h3> This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al.,  2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021).  WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020. Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the two simulation with snow cover, where  jan126_sms.zip contains the files relating to the simulations with the SMS-3DTKE scheme and jan16.zip those for the simulation with the MYNN 2.5-order PBL parameterization. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the files namelist.input and namelist_sms.input that were used for the simulations.  Standard WRF output can be found in wrfout_40m_jan16 and wrfout_40m_jan16_sms. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in windout_40m_jan16 and windout_40m_jan16_sms. These variables were contained in the  unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in tend_40m_jan16.nc and tend_40m_jan16_sms.nc. <h3>Plotting routines</h3> Python scripts and environment files to reproduce most figures in Rauchoecker et al. (2024d) are included in code.zip. To reproduce plots involving measurement data, which is available in Rauchöcker et al. (2024c), is also needed. Due to conflicts between some packages, two different environment were needed. To reproduce Figure 2, install the orthoplot environment by running "conda env create orthoplot.yml" in a terminal window, activate it  ("conda activate orthoplot") and then run ortho_plot.py. All other plots require the wrfstuff environent (installed by running "conda env create wrfstuff.yml" and activated by "conda activate wrfstuff") and are produced by paper_plots.py. Functions used to load data and plot the figures are included in dataload.py and plotting_routines.py, respectively. Two variables decide which figures are plotted for which dataset: dataname and doplot. The variable dataname defines the path to the dataset that should be used to produce the figures, while the value doplot defines which figure to reproduce. By setting doplot="fig1", Figure 1 is reproduced, while doplot="fig7" and doplot="fig9" reproduce Figures 7 and 9, respectively. For all other values for doplot, Figures 4, 5, 6, 8 and 11 are reproduced. We decided not to include a script to plot Figure 3 because the data the climatology is based on is owned by GeoSphere Austria - the agency operating the permanent weather station. Further, no script for reproducing Figure 10 is included because it was not created within the framework of Python. <h3>Geofiles</h3> The files included in geofiles.zip are needed to plot Figure 1 and Figure 2, although not of particular interest on their own. Included are output files from geogrid.exe, whicih are necessary to plot the domains overview (Figure 1), as well as an orthophoto (orthophoto.tif) and high-resolution digital elevation model (topo_hr.tif) which are both based on data from Land Tirol.

opencc-by-4.0Oct 2024View details →

The Application of a Snowpack Runoff Decision Support System for Rain-on-Snow Events Dataset

This work was funded by the State of Nevada - Department of Transportation award No. P296-22-803 and UCAR COMET Outreach Program SUBAWD004566.

opencc-by-4.0Oct 2020View details →

Snow cover in the European Alps: Station observations of snow depth and depth of snowfall

Auxiliary files, code, and data for paper published in The Cryosphere: Observed snow depth trends in the European Alps 1971 to 2019  <a href="https://doi.org/10.5194/tc-15-1343-2021">https://doi.org/10.5194/tc-15-1343-2021</a>   Auxiliary files: <ul> <li>aux_paper.zip: Auxiliary figures to the paper (time series showing the consistency of averaging monthly mean snow depth of stations within 500 m elevation bins; times of seasonal snow depth and snow cover duration indices).</li> <li>aux_paper_crocus_comparison.zip: Time series comparing spatial statistical gap filling from paper to gap filling using snow depth assimilation into Crocus snow model (only for subset of stations in the French Alps)</li> <li>aux_paper_monthly_time_series.zip: Plots of monthly time series of snow depth, for each station.</li> <li>aux_paper_spatial_consistency.zip: Aggregate results from spatial consistency (statistical simulation using neighboring stations), and time series of observed versus simulated monthly snow depths.</li> </ul>   Code (working copy, not cleaned, all written in R statistical software): code.zip <ul> <li>to read in the different data sources</li> <li>to do quality checks and data processing</li> <li>to perform statistical analyses as in paper</li> <li>to produce figures and tables as in paper</li> </ul>   Data: <ul> <li>> 2000 stations from Austria, Germany, France, Italy, Switzerland, and Slovenia</li> <li>Daily stations snow depth and depth of snowfall, as .zips, grouped by data provider. Information on column content is provided in "data_daily_00_column_names_content.txt".</li> <li>Monthly stations mean snow depth, sum of depth of snowfall, maximum snow depth, days with snow cover (1-100cm thresholds), as .zips, grouped by data provider. Information on column content is provided in "data_monthly_00_column_names_content.txt".</li> <li>Meta data (name, latitude, longitude, elevation) in "meta_all.csv", along with an interactive map "meta_interactive_map.html", and column information in "meta_00_column_names_content.txt".</li> <li>If you use the data you agree to adhere to the respective data provider's terms as listed in "00_DATA_LICENSE_AND_TERMS.PDF"</li> <li>The license terms especially (and additionally to any other terms of the single data providers) include: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. [from <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>] </li> </ul>     Version history: v1.3: added maxHS and SCD (with various 1-100cm thresholds) to monthly data v1.2: uploaded data v1.1: changes to aux-paper.zip and code.zip as consequence from submitting a revised manuscript v1.0: initial upload

opencc-by-4.0Aug 2021View details →

Data set of anthropogenic contaminants in snow from polar regions (Ny-Alesund and Dome C)

The produced dataset (in MS Excel format) contains concentrations of mercury, trace elements and organic contaminants in snow samples collected in the Ny-Alesund area (Svalbard - Norway) (78.917° N 11.933° E) and from the Antarctic Plateau, Dome C (75.103°S, 123.35°E). The Arctic sampling sites are reported in figure 1. The concentrations for trace elements and mercury are in ngg-1 while for the organic contaminants they are reported in ngL-1. The inorganic contaminants dataset reports concentration of Hg, Trace elements and Black Carbon in Arctic and Antarctic site. The Arctic sites are subdivided in annual snow pack on the glacier and surface snow sampling close to the Gruvebadet Aerosol Laboratory. In Antarctica mercury concentrations in surface snow are also reported. The organic contaminants dataset reports the concentrations of Polycyclic Aromatic Hydrocarbons (PAHs) in surface snow samples collected close to the Gruvebadet Aerosol Laboratory (78.91622°N 11.89536°E, Ny Alesund, Norway). Samplings were performed from 04/10/2018 to 13/05/2019, obtaining a total of 35 samples, encompassing the entire winter season with an approximatively weekly resolution. Total PAH (sum of naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benzo(a)anthracene, chrysene, benzo(b)fluoranthene, benzo(k) fluoranthene, benzo(a)pyrene, benzo(ghi)perylene, indeno(1,2,3-c,d)pyrene and dibenzo(a,h)anthracene) concentrations range from 0.8 to 37 ng L-1. Individual PAHs were mean blank corrected and average percentage abundances in the samples are reported in the dataset.

opencc-by-4.0Aug 2021View details →

Data for "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia"

Data and code used for a country-wide occupancy survey of snow leopards in Mongolia, accompanying the paper "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia". This data contains the results of a survey of 1017 20x20km sampling units, out of a total of 1200 sampling units identified as potential snow leopard habitat (183 could not be sampled for various reasons), a near complete survey of potential snow leopard habitat in Mongolia, nearly 500,000 square kilometers, and an enormous effort by many researchers. If you make use of the data, please cite the following sources: <ul> <li>Data for "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia". (2021). Gantulga Bayandonoi, Koustubh Sharma, Justine Shanti Alexander, Purevjav Lkhagvajav, Ian Durbach, Darryl MacKenzie, Chimeddorj Buyanaa, Bariushaa Munkhtsog, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Bilguun Batkhuyag, Nyamzav Battulga, Choidogjamts Byambasuren, Bayartsaikhan Uudus, Shar Setev, Lkhagvasuren Davaa, Khurel-Erdene Agchbayar, Naranbaatar Galsandorj, David Borchers. doi: https://doi.org/10.5281/zenodo.5257572</li> <li>Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia. (2021). Gantulga Bayandonoi, Koustubh Sharma, Justine Shanti Alexander, Purevjav Lkhagvajav, Ian Durbach, Darryl MacKenzie, Chimeddorj Buyanaa, Bariushaa Munkhtsog, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Bilguun Batkhuyag, Nyamzav Battulga, Choidogjamts Byambasuren, Bayartsaikhan Uudus, Shar Setev, Lkhagvasuren Davaa, Khurel-Erdene Agchbayar, Naranbaatar Galsandorj, David Borchers. To appear in Diversity and Distributions</li> </ul> Contents of zip file Data The main dataset is contained in `data\Mongolia_occupancy_inputs.Rdata` . Please see the paper for more detail on data collection. The following objects are contained in the file: - Pres: presence/absence occupancy survey results, used for model fitting - Site_Cov: unit-specific covariates, used for model fitting - SurvCov: survey-specific covariates, used for model fitting - Mongolia_studyarea: covariates for whole survey area, used for prediction - Mongolia_fullrange: covariates across whole expected snow leopard range, used for prediction Code Code is cloned from the GitHub repository <a href="https://github.com/iandurbach/mongolia-occupancy">https://github.com/iandurbach/mongolia-occupancy</a>, which may contain updates. The version here reproduces the analyses in the paper above. The run these analyses: - run *occupancy-analysis.R* to fit the main occupancy models (these are also saved in the `\output` folder), do model selection, and plot covariate effects - run *occupancy-goodness-of-fit.R* to calculate the c-hat statistic giving an indication of model fit for the best model - run *comparing-maps.R* to compare the occupancy results with similar metrics generated using a presence-only analysis (using MaxEnt) or an expert map generated through qualitative discussion (reproduces Figure 3 in the paper). Code in *occupancy-data-preproc.R* is not needed but included for completeness. It converts the csv files in `data\csv`, which contain various input datasets used by the occupancy model, into a single .Rdata file (`data\Mongolia_occupancy_inputs.Rdata`), which is then used by the scripts above. Some minimal pre-processing (excluding ununsed variables, renaming for consistency, etc) is performed.

opencc-by-4.0Sep 2022View details →

MODIS Snow-Cover Frequency Maps

The 365 global snow cover frequency maps were derived from MODIS MOD10C1 data.  There is one map for each day of the year (leap year days are excluded).  Each map displays the frequency of snow cover for the 20-year time series, Hydrological Years 2000 - 2020, on a per-grid cell basis.  Each grid cell is approximately 5 X 5 km.  The dataset is described in detail in Riggs et al. (in press).

opencc-by-4.0Jul 2022View details →

25 years of high-frequency ground penetrating radar measurements of snow studies in Svalbard - metadata and GPS tracks

Surveys by ground penetrating radar (GPR) are accurate and cost-efficient, and have been conducted on Svalbard for more than 25 years, thus permitting the assessment of long term changes. The campaigns so far have covered various areas and the data is dispersed. The purpose of this report is to collect information about the conducted GPR snow cover measurements. The activities initiated in this project will be continued in the coming years and extended with a comprehensive data analysis. The dataset includes a description of metadata from GPR snow cover measurements in 1997-2022 (.CSV file) and GPS traces (.SHP files) of measurements taken in Svalbard. This study is part of the State of Environmental Science in Svalbard Report 2022 published by Svalbard Integrated Arctic Earth Observing System (SIOS).

opencc-by-4.0Dec 2022View details →

Data from: Nest orientation and proximity to snow patches are important for nest site selection of a cavity breeder at high elevation

Abstract Reproductive timing and location are central to breeding success across taxa. Many species have evolved specific strategies to cope with environmental variability including shifts in timing of reproduction tracking resource availability or selecting favourable nest location. In mountain ecosystems, complex topography and pronounced seasonality result in particularly high spatiotemporal variability of environmental conditions, and the risk of climate-induced resource mismatches is particularly acute given that temperature is increasing more rapidly than in the lowlands. We investigated how a high-elevation passerine, the white-winged snowfinch Montifringilla nivalis, selects its nest site in relation to nest cavity characteristics, habitat composition and snow condition. We used a combination of field habitat mapping and satellite remote sensing to compare occupied nest sites with randomly selected pseudo-absence sites. In the first half of the breeding season, snowfinches preferred nest cavities oriented towards the morning sun while they used cavities proportional to their availability later on. This preference might relate to the nest microclimate offering eco-physiological advantages, namely thermoregulatory benefits for incubating adult and nestlings under the harsh conditions typically encountered in the alpine environment. Nest sites were consistently located in areas with greater-than-average snow cover at hatching date, likely mirroring the foraging preferences for tipulid larvae developing in meltwater along snowfields. Due to the particularly rapid climate shifts typical of mountain ecosystems, spatiotemporal mismatches between foraging grounds and nest sites are expected in the future, which may negatively influence demographic trajectories of the species concerned. The installation of well-designed nest boxes in optimal habitat configurations could to some extent help mitigate this risk.

opencc-by-4.0Dec 2022View details →

NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series and the regionalisation of the ΔSNOW model

Time series of daily Snow Water Equivalent (SWE) and Snow Density over the Northern Hemisphere, based on in-situ station observations of snow depth converted to SWE using the ΔSNOW model (Winkler et al., 2021) and regionalised parameters.  An extensive description of the dataset and the method to generate it can be found in the data descriptor manuscript published in the journal Earth System Science Data: <a href="https://essd.copernicus.org/preprints/essd-2023-31/">https://essd.copernicus.org/articles/15/2577/2023/essd-15-2577-2023</a>  Dataset: A total of 11,0071 time series of modelled SWE and estimated snow density at the point scale, spanning 1950-2022, at daily resolution. "NH-SWE_dataset_MAP.png" shows a Northern Hemisphere map with the location of all stations in the NH-SWE dataset and their elevation in meters.  Files: The dataset is provided in two different formats: <ol> <li>Individual .csv files for each station in the NH-SWE dataset at "NH_SWE_dataset_vector_files.zip"</li> <li>Full-dataset .csv matrices with dates as rows and NH-SWE stations as columns at "NH_SWE_dataset_matrix_files.zip"</li> </ol> Metadata: "NH_SWE_METADATA.csv" Includes information on NH-SWE stations location (ID, country, station name, coordinates, elevation), data source, length of time series, model parameters and the climate variables used to estimate them, and average snow climatology such as average maximum snow depth, average peak SWE and average maximum snow cover duration. More details and units in the "README_fileformats.txt" file.  ΔSNOW model parameter regionalisation: The code to obtain the ΔSNOW model parameters based on climate variables for all the stations in the NH-SWE dataset is shared in "DeltaSNOW_parameter_regionalisation.zip". The method is extensively described in the data descriptor manuscript by Fontrodona-Bach et al., (2023) submitted to Earth System Science Data. More details in the "README_regionalisation.txt" file.  Data use: Free, provided adequate citation of both the data descriptor manuscript and the zenodo record. See "README_datausage.txt" Version history: v1: Initial upload. The ΔSNOW model regionalisation was missing. v2: Manuscript submission version. Updated dataset and includes the ΔSNOW model regionalisation code. Reported errors: The dataset accidentally contains one station from the Southern Hemisphere (NH-SWE ID 500001), located in Antarctica (Country code AY).  The longitude of a few stations exceeds +180 decimal degrees. To obtain the correct value within the [-180,180] decimal degree longitude bounds, the value exceeding +180 needs to be added to -180 degrees (e.g. +181.0 degrees is actually -179.0 degrees). Swedish stations have two different country codes, SE for the ECA&D stations, and SW for the GHCNd stations.  Japan country code is "JA" in the metadata, although the official country code should be JP.

opencc-by-4.0Oct 2023View details →

SNOWISO model snow- and firn core simulations for the EastGRIP drilling site in Greenland

This dataset (.csv) includes four SNOWISO v2 snowpack simulations of the stable water isotopes (δ18O, δD, d-excess) and is the result of snowpack simulations in: Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, <a href="https://doi.org/10.1029/2023GL104249">http</a><a href="https://doi.org/10.1029/2023GL104249">s://doi.org/10.1029/2023GL104249</a> The SNOWISO model is a 1-D isotope-enabled snowpack and surface exchange model. The model accumulates snowfall (input) and applies water vapor exchange (input) at the snow surface with subsequent isotopic fractionation of the surface snow. In addition, diffusion of water isotopes in the accumulated snowpack is applied. This dataset is simulated in a 1 cm vertical layer resolution. The scientific theory of the SNOWISO model is described in: Wahl, S., Steen‐Larsen, H.C., Hughes, A.G., Dietrich, L.J., Zuhr, A., Behrens, M., Faber, A.K. and Hörhold, M., 2022. Atmosphere‐Snow Exchange Explains Surface Snow Isotope Variability. Geophysical Research Letters, 49(20), p.e2022GL099529. The documentation of the SNOWISO model v2 operational set-up is given in: Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, <a href="https://doi.org/10.1029/2023GL104249">https://doi.org/10.1029/2023GL104249</a> This model dataset consists of simulations for two model configurations each, with (control) and without (no_frac) fractionation during vapor exchange:   <ol> <li>daily average isotopes in the surface snow (top 2 cm) for the periods 11/05/2018-5/8/2018 and 17/5/2019-31/7/2019 <ul> <li>surface_snow_simulation_2018-2019_control.csv</li> <li>surface_snow_simulation_2018-2019_no_frac.csv</li> </ul> </li> <li>three 1-m long snow cores ending in 2017, 2018, and 2019, respectively <ul> <li>snowpack_core_simulation_2017_control.csv</li> <li>snowpack_core_simulation_2018_control.csv</li> <li>snowpack_core_simulation_2019_control.csv</li> <li>snowpack_core_simulation_2017_no_frac.csv</li> <li>snowpack_core_simulation_2018_no_frac.csv</li> <li>snowpack_core_simulation_2019_no_frac.csv</li> </ul> </li> <li>one firn core simulation in the period 1990-2011 (~6 m) <ul> <li>snowiso_model_1990-2012_control.csv</li> <li>snowiso_model_1990-2012_no_frac.csv</li> </ul> </li> <li>one firn core simulation in the period 1990-2020 (~8.5 m) <ul> <li>snowiso_model_1990-2020_control.csv</li> <li>snowiso_model_1990-2020_no_frac.csv</li> </ul> </li> </ol> Model input: <ul> <li>6-hourly precipitation rate, vapor, and precipitation water stable isotopes from ECHAM6-wiso simulation nudged to the ERA-5 reanalysis (https://zenodo.org/record/8341390)</li> <li>hourly latent heat flux, near-surface meteorological variables, and snowpack variables from MARv3.12 simulation driven by the ERA-5 reanalysis (https://zenodo.org/record/8335402)</li> </ul> Please be encouraged to contact me (Laura.Dietrich@uib.no) if you have any questions or ideas regarding these SNOWISO model simulations. Data usage notice: When using the SNOWISO model, you should refer to: Wahl, S., Steen‐Larsen, H.C., Hughes, A.G., Dietrich, L.J., Zuhr, A., Behrens, M., Faber, A.K. and Hörhold, M., 2022. Atmosphere‐Snow Exchange Explains Surface Snow Isotope Variability. Geophysical Research Letters, 49(20), p.e2022GL099529. If you use any of these simulations, you should refer to: Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, <a href="https://doi.org/10.1029/2023GL104249">https://doi.org/10.1029/2023GL104249</a>

openOpenMar 2007View details →

Air Temperature, Soil Temperature, Precipitation, Snow Depth at Long Term Tree Growth Sites; 1968-Present : Weekly

Part of the Long Term Tree Growth study. This dataset is an accumulation of various manual measurements made on a weekly to monthly basis. It originally included snow stakes, rain buckets, max/min thermometers and a series of soil temperature sensors. Over the years equipment has changed. The soil temperature sensors exceeded their field life during the 1990's and were dropped from the study. In 2001 logging air temperature and relative humidity sensors were installed and those measurements were discontinued. In 2002 logging rain gauges were installed to replace the manual buckets. Both styles were during that growing season and the manual buckets were removed before the 2003 field season. All that remains active in this dataset are the snow stake measurements.

openCC (other)Oct 2021View details →

Hubbard Brook Experimental Forest: Soil Freezing Study (SFS) In Situ Measurements of Snow and Soil Frost Depth

Climate models for the northeastern United States (U.S.) over the next century predict an increase in air temperature between 2.8 and 4.3 °C and a decrease in the average number of days per year when a snowpack will cover the forest floor (Hayhoe et al. 2007, 2008; Campbell et al. 2010). Studies of forest dynamics in seasonally snow-covered ecosystems have been primarily conducted during the growing season, when most biological activity occurs. However, in recent years considerable progress has been made in our understanding of how winter climate change influences dynamics in these forests. The snowpack insulates soil from below-freezing air temperatures, which facilitates a significant amount of microbial activity. However, a smaller snowpack and increased depth and duration of soil frost amplify losses of dissolved organic C and NO3- in leachate, as well as N2O released into the atmosphere. The increase in nutrient loss following increased soil frost cannot be explained by changes in microbial activity alone. More likely, it is caused by a decrease in plant nutrient uptake following increases in soil frost. We conducted a snow-removal experiment at Hubbard Brook Experimental Forest to determine the effects of a smaller winter snowpack and greater depth and duration of soil frost on trees, soil microbes, and arthropods. A number of publications have been based on these data: Comerford et al. 2013, Reinmann et al. 2019, Templer 2012, and Templer et al. 2012. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Campbell JL, Ollinger SV, Flerchinger GN, Wicklein H, Hayhoe K, Bailey AS. Past and projected future changes in snowpack and soil frost at the Hubbard Brook Experimental Forest, New Hampshire, USA. Hydrological Processes. 2010; 24:2465–2480. Comerford DP, PG Schaberg, PH Te

openCC (other)Jun 2021View details →

Measurements of microbial biomass and activity in two snow manipulation experiments at Hubbard Brook Experimental Forest 1998 – 2004

In 1997, as part of a study of the relationships between snow depth, soil freezing and nutrient cycling (http://www.ecostudies.org/people_sci_groffman_snow_summary.html), we established eight 10 x 10-m plots located within four stands; two dominated (80%) by sugar maple and two dominated by yellow birch, with one snow reduction (freeze) and one reference plot in each stand. In 2001, we established eight new 10-m x 10-m plots (4 treatment, 4 reference) in four new sites; two high elevation, north facing and two low elevation, south facing maple-beech-birch stands. To establish plots for the “freeze” study, we cleared minor amounts of understory vegetation from all (both freeze and reference) plots (to facilitate shoveling). We then installed soil solution samplers (zero tension lysimeters), thermistors for soil temperature monitoring, water content (time domain) reflectometers (for measuring soil moisture), soil atmosphere sampling probes, minirhizotron access tubes, and trace gas flux measurement chambers (described below). All plots were equipped with dataloggers to allow for continuous monitoring of soil moisture and temperature. Treatments (keep plots snow free by shoveling through the end of January) were applied in the winters of 1997/98, 1998/99, 2002/2003 and 2003/2004. Measurements of soil nitrate (NO3 -) and ammonium (NH4 +) concentrations, microbial biomass carbon (C) and nitrogen (N) content, microbial respiration, potential nitrification and N mineralization rates, pH, and denitrification potential were measured on these plots at multiple time points during these studies. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES) using funding from the U.S. National Science Foundation. The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)Jun 2025View details →

Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: Soil Temperature, Soil Frost, and Snow Depth Data in support of "Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems", Conrad-Rooney et al. PNAS 2025

Data associated with the publication: Conrad-Rooney E, AB Reinmann, PH Templer. Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems. Proceedings of the National Academy of Sciences, 2025. This dataset includes soil temperature (winter 2021-2022) and snow depth and frost depth (winter 2022-2023) at the Climate Change Across Seasons Experiment. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)Mar 2025View details →

Glacier snow depth measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)

As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package includes snow depth measurements to the surface of six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys of Antarctica. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.