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966 results for “snow”
Projected Snow Cover Reductions and Mid-latitude Cyclone Responses in the North American Great Plains, 1986 - 2005
Extratropical cyclones are responsible for major weather events and trends in the mid-latitudes and preferentially develop in regions of enhanced cyclogenesis and proceed along climatological storm tracks. It has been shown that terrestrial snow cover exerts considerable influence on atmospheric baroclinicity which is largely responsible for the aforementioned cyclogeneses and storm tracks. Research about the effect which terrestrial snow cover exerts on cyclones' intensities, trajectories, and precipitation characteristics is limited but indicates a robust relationship with these factors. Many examinations of climate model projections have generally shown a poleward shift in storm tracks by the late 21st century though none have determined the degree to which the coincident poleward shift in snow extent is responsible. A method of imposing 10th, 50th, and 90th percentile values of snow retreat between the late 20th and 21st centuries as projected by 14 models of the Coupled Model Intercomparison Project Phase Five (CMIP5) is used to alter 20 historical cold season cyclones which tracked over or adjacent to the North American Great Plains. Simulations by the Advanced Research version of the Weather Research and Forecast Model (WRF-ARW) are initialized at 0 to 4 days prior to cyclogenesis. Including control and sensitivity testing wherein snow is unaltered or removed entirely, each cyclone case is simulated 25 times for a total of 500 simulations.
Snow depth and snow water equivalent measurements along a road course and historic snow course in the Andrews Experimental Forest, 1978 to present
With an increase in emphasis on monitoring climate change impacts and change in the form of precipitation at HJ Andrews Experimental Forest, snow data collection within our climate monitoring program, a snow course to document depths of snow was designed around a dispersed sampling scheme rather than a point intensive scheme as previously employed in the historic Reference Stand snow course. Primary objectives are to document the presence/absence of snow, snow depth, and time of melt-off. Snow depths are verified using stakes placed near the road to allow for routine and frequent observation. Stakes are placed at different locations, elevations and aspects in paired forested/open sites. Time-lapse cameras were deployed at all the stakes to allow for daily measurements beginning in fall 2014. Truthing of points with snow core sampling for snow moisture content (snow water equivalent) is done when possible, usually 1-2 times per year. Cameras are set to take 3 readings per day (09:00, 12:00, 15:00 PST). One snow depth and coverage is extracted from the images per stake per day.
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).
Hubbard Brook Experimental Forest: Weekly Snow and Frost Measurements, 1955 - present
Snow and frost measurements have been collected approximately weekly during the winter season at the Hubbard Brook Experimental Forest using transects underneath the forest canopy adjacent to the established network of standard rain gages from 1956 to the present. Maximum snow depth, snow water content, frost occurrence, and frost depth data are recorded at points along a transect known as a snow course, which includes 10 points spaced at 2-m intervals within a designated 0.25 ha area. Data from one course are averaged for each collection date. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Sap Flow in Red Maple and Red Oak in the Harvard Forest Snow Removal Study 2011
The climate is changing in mid and high latitude environments with the depth and duration of snowpack shrinking for many temperate forest ecosystems. A reduced snowpack and increased depth and duration of soil frost can injure fine roots, which are essential for plant water uptake. Water uptake is a crucial component of ecosystem functioning because this process strongly impacts other biological processes, such as primary productivity and nutrient uptake. We evaluated the effects of changing winter climate, including snow and soil frost dynamics, on rates of water uptake (i.e. sap flow) in a snow manipulation study at Harvard Forest. We had three reference and tree plots from which we removed snow and induced soil freezing.
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).
Snow depth data for Saddle grid, 1992 - ongoing.
The depth of snow was measured at 88 points on the saddle grid. The 500 x 350 m study area (17.5 ha) consisted of a grid of 8 rows of stakes in an east/west direction and 11 rows of stakes in a north/south direction (for a total of 88 stakes). The stakes were located 50 m apart. Each stake was given a point identification number starting with 1 in the southwest corner and progressing in an easterly direction for each of the east/west rows so that if head of this file represented the north compass point and the tail represented the south compass point, then the grid would look like this: 71 72 73 74 75 76 77 78 79 80 801(=80A) 61 62 63 64 65 66 67 68 69 70 701(=70A) 51 52 53 54 55 56 57 58 59 60 601(=60A) 41 42 43 44 45 46 47 48 49 50 501(=50A) 31 32 33 34 35 36 37 38 39 40 401(=40A) 21 22 23 24 25 26 27 28 29 30 301(=30A) 11 12 13 14 15 16 17 18 19 20 201(=20A) 1 2 3 4 5 6 7 8 9 10 101(=10A) Note that stakes along the east boundary of the grid, i.e. those ending with the "A", were given new designations to facilitate incorporation of the data into the Saddle GIS. Snow depths at each of the stakes were recorded on a weekly to biweekly basis throughout the period during which snow accumulation existed on the Saddle.
Snow depth data for saddle snowfence, 1992 - ongoing.
A snowfence was built in 1993 on the Niwot Ridge Saddle grid to determine the effects of changes in snowpack on a number of variables. The study area was 60m x 125m. Snow depths were measured at each of 147 points within the snowfence experiment area and at 6 control locations outside of but near the snowfence experiment area. Of the 147 points, 84 were located on the leeward (east) side of the snowfence, 56 were located on the windward (west) side of the snowfence, and 7 were located along the snowfence itself. These measurements were made weekly to biweekly. Sampling locations were each given a unique point identification number so that these data could be incorporated into the Saddle GIS. The snowfence was oriented in a north/south direction and was 60 m long. Each of the point identification numbers had a coordinate within the experiment area. The first number of the coordinate was the distance in m from the snowfence in an east/west direction, negative numbers being west of the snowfence and positive numbers being east of the fence. The second number in the coordinate was the distance in m from the southern terminus of the snowfence in a northerly direction.
Bonanza Creek LTER: Hourly Snow Pillow Measurements from 2007 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska
The snow pillow records the hourly water content of the snowpack (snow water equivalent) at the CARSNOW site within the Caribou Poker Creeks Research Watershed during the winter months. It consists of two 1m square aluminium "pillows" filled with a propylene glycol/water solution attached via piping to a druck pressure transducer. The pressure on the pillow is converted to cm of water. A manometer tube is also attached for manaul readings and calibration.
Harvard Forest Snow Pillow since 2009
Water storage in snowpack is a key factor in the hydrological cycle of central New England. The Harvard Forest Snow Pillow provides continuous long-term measurements of the water content of snowpack (snow water equivalent) during the winter months. The snow pillow is located in a mature mixed hardwood stand with scattered conifers about 50 m north of the Nelson Brook Big Weir. For current data, please see: https://harvardforest.fas.harvard.edu/met-hydro-stations.
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.
Snow Manipulation Greenhouse Gas Measurements at South Sparkling and Trout Bog 2020-2021
To investigate the effect of a winter with decreased snow cover on greenhouse gas emissions, we experimentally removed snowfall from a small dystrophic lake in northern Wisconsin. As a comparative study, we were able to explore the role of light in under-ice gas dynamics and spring emissions in dimictic lakes. This dataset contains greenhouse gas and temperature/dissolved oxygen profile data collected on South Sparkling and Trout Bog during the winter of 2020 through the winter of 2021. Data were collected between 09 January 2020 and 13 April 2021 in the deep hole of both bogs. Dissolved greenhouse gas concentrations of carbon dioxide and methane were measured using the headspace equilibrium method.
Snow water equivalent data for Niwot Ridge and Green Lakes Valley, 1993 - ongoing.
Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. This dataset contains derived values of SWE from snow profile measurements.
Snow grain data for Niwot Ridge and Green Lakes Valley, 1995 - ongoing.
Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season.
Snow cover profile data for Niwot Ridge and Green Lakes Valley, 1993 - ongoing.
Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season.
Retrieved snow depth in Mainland Norway (2018.10-2022.10) based on ICESat-2 ATL08 and DEMs
<h3><strong>Introduction</strong></h3> <p>This dataset's snow depth data was derived using elevation differencing, which is simply the snow surface elevation (ICESat-2 ATL08) minus the reference surface elevation (obtained from Digital Elevation Models):</p> <ol> <li><strong>DEM Co-registration</strong>: DEMs are co-registered to ICESat-2 ATL08 snow-off reference without vertical bias adjustment.</li> <li><strong>Elevation Bias Correction</strong>: The elevation bias between the DEMs and ICESat-2 is corrected using ICESat-2 ATL08 snow-off segments.</li> <li><strong>Snow Depth Calculation</strong>: Determining snow depth by subtracting <strong>the bias-free reference ground elevation(from Step 2)</strong> from ICESat-2 ATL08 snow-on segments.</li> </ol> <p>This dataset is presented in a tabular format, which simplifies the preprocess for machine learning models. While co-registration has been done (1), users have the flexibility to train a bias correction model again (2) and retrieve snow depth measurements anew (3). Alternatively, the snow depth can be directly used for various analytical purposes. Detailed methodologies for the co-registration, bias correction, and snow depth determination are thoroughly documented in the paper (under submission) to support users in leveraging this dataset for their research needs.<br> </p> <h3><strong>Meta Information</strong></h3> <ul> <li><strong>Study Area</strong>: Mainland Norway</li> <li><strong>Acquisition Period (ICESat-2)</strong>: October 2018 to October 2020</li> <li><strong>ICESat-2 data source</strong>: ATL08 (level3, version 5)</li> <li><strong>Reference DEMs</strong>: Norway DTM1, Norway DTM10, Copernicus GLO30, FABDEM. (see reference links)</li> <li><strong>Reference snow depth: </strong>ERA5 Land (hourly), ERA5 Land (monthly).</li> <li><strong>Snow condition</strong>: The dataset contains snow depth retrieved (snow_on_alt08_segments_and_snow_depth.csv) and snow-free observations (snow_free_alt08_segments_and_dems.csv).</li> <li><strong>Data Cleaning</strong>: No, this is a raw dataset that may contain outliers.</li> <li><strong>Mask</strong>: Excluded water surface and permanent ice at a spatial resolution of 100 m. </li> </ul> <h3><strong>Description</strong></h3> <p>This dataset encapsulates a wide array of attributes derived from ICESat-2 observations, alongside measurements pertinent to snow depth, terrain, and environmental conditions across Mainland Norway. For detailed attribute descriptions, refer to the <a href="https://nsidc.org/data/atl08/versions/5#anchor-2">ICESat-2 ATL08 documentation</a>. The dataset is structured into several columns, each representing a specific attribute:</p> <ol> <li>'latitude': Latitude coordinates of the data points in WGS 84.</li> <li>'longitude': Longitude coordinates of the data points in WGS 84.</li> <li>'segment_landcover': Land cover classification for each segment.</li> <li>'segment_snowcover': Snow cover classification for each segment.</li> <li>'h_te_best_fit': Best-fit elevation of the terrain.</li> <li>'h_te_std': Standard deviation of terrain elevation.</li> <li>'n_te_photons': Number of photons used for terrain elevation estimation.</li> <li>'subset_te_flag': Quality flag (5 = all geosegments available, 4 = four geosegments...).</li> <li>'segment_cover': Woody vegetation fractional cover derived from the 2019 Copernicus 100m shrub and forest fractional cover data product.</li> <li>'h_canopy': Canopy height above terrain from ICESat-2 (only for snow-off segments).</li> <li>'h_mean_canopy': Mean canopy height ICESat-2 (only for snow-off segments).</li> <li>'canopy_openness': Canopy openness from ICESat-2 (only for snow-off segments).</li> <li>'h_canopy_winter': Canopy height above terrain from ICESat-2 (only for snow-on segments).</li> <li>'h_mean_canopy_winter':Canopy mean height from ICESat-2 (only for snow-on segments).</li> <li>'canopy_openness_winter':Canopy openness from ICESat-2 (only for snow-on segments).</li> <li>'tree_presence': the presence of trees in the segment (1 = tree, 0 = no tree, binary of h_canopy).</li> <li>'pair': Pair flag for ICESat-2.</li> <li>'beam': Beam flag for ICESat-2.</li> <li>'p_b': Pair and beam flag for ICESat-2.</li> <li>'region': Region identifier for ICESat-2.</li> <li>'cloud_flag_atm': Atmospheric cloud flag for ICESat-2.</li> <li>'urban_flag': Urban area flag for ICESat-2.</li> <li>'h_te_skew': Skewness of terrain elevation of segments.</li> <li>'snr': Signal-to-noise ratio for ICESat-2.</li> <li>'terrain_slope': Slope of the terrain from ICESat-2.</li> <li>'h_te_uncertainty': Uncertainty in terrain elevation estimation.</li> <li>'night_flag': Flag indicating nighttime data.</li> <li>'brightness_flag': Brightness flag for ICESat-2.</li> <li>'h_te_interp': Interpolated terrain elevation.</li> <li>'E': Easting coordinate in EPSG 32633.</li> <li>'N': Northing coordinate in EPSG 32633.</li> <li>'slope': Terrain slope computed from DTM10.</li> <li>'aspect': Terrain aspect computed from DTM10.</li> <li>'planc': Plan curvature computed from DTM10.</li> <li>'profc': Profile curvature computed from DTM10.</li> <li>'curvature': Overall terrain curvature computed from DTM10.</li> <li>'tpi': Terrain Position Index computed from DTM10.</li> <li>'tpi_9': TPI with a 90-meter radius.</li> <li>'tpi_27': TPI with a 270-meter radius.</li> <li>'wf_positive': Positive wind aspect index.</li> <li>'wf_negative': Negative wind aspect index.</li> <li>'smlt_acc': Snowmelt accumulation calculated from ERA5 Land monthly snow melting (currently not in use).</li> <li>'sf_acc': Snowfall accumulation calculated from ERA5 Land monthly snowfall (currently not in use).</li> <li>'sd_era': Snow depth from ERA5 Land reanalysis, coupled with ICESat-2 measurements at daily resolution,</li> <li>'sde_era': Snow depth linear interpolated from ERA5 Land reanalysis.</li> <li>'date': Date of data acquisition.</li> <li>'date_': Date in Pandas Datatime data dype.</li> <li>'month': Month of data acquisition.</li> <li>'difference': The elevation difference between segment and subsegment at the midpoint ( 'h_te_best_fit_20m_2' minus 'h_te_best_fit'). If you want to use h_te_best_fit_20m_2 instead of h_te_best_fit as elevation from ICESat-2, you can do it by df_after_dtm1 - difference, snowdepth_dtm1 - difference.</li> </ol> <p>Columns on elevation difference and snow depth (in meters):</p> <ol> <li>'<strong>dh_after_dtm1</strong>': The elevation difference between the snow-free segment and DTM1 (ICESat-2 minus DTM1). This serves as an independent variable y in the bias correction model for DTM1. Here, 'after' means after co-registration.</li> <li>'<strong>snowdepth_dtm1</strong>': The elevation difference between the snow-on segment and DTM1 (ICESat-2 minus DTM1), representing the raw snow depth as measured against DTM1.</li> <li>'<strong>sd_correct_dtm1</strong>': Corrected snow depth using DTM1, adjusted by bias correction model.</li> <li>'<strong>df_dtm1_era5</strong>': Difference betwen 'sd_correct_dtm1' and 'sde_era'. (sd_correct_dtm1 minus sde_era), providing a comparison between corrected snow depth from DTM1 and snow depth from ERA5 Land reanalysis</li> <li><strong>'dh_after_dtm10'</strong>: The elevation difference between the snow-free segment and DTM10 (ICESat-2 minus DTM10), used in bias correction for DTM10.</li> <li><strong>'snowdepth_dtm10'</strong>: The elevation difference between the snow-on segment and DTM10 (ICESat-2 minus DTM10).</li> <li><strong>'sd_correct_dtm10'</strong>: Corrected snow depth using DTM10, adjusted by bias correction model.</li> <li><strong>'df_dtm10_era5'</strong>: Difference between 'sd_correct_dtm10' and 'sde_era'.</li> <li><strong>'dh_after_cop30'</strong>: The elevation difference between the snow-free segment and Copernicus GLO30 (ICESat-2 minus Copernicus GLO30).</li> <li><strong>'snowdepth_cop30'</strong>: The elevation difference between the snow-on segment and Copernicus GLO30.</li> <li><strong>'sd_correct_cop30'</strong>: The adjusted snow depth using Copernicus GLO30, adjusted by bias correction model.</li> <li><strong>'df_cop30_era5'</strong>: The discrepancy between 'sd_correct_cop30' and 'sde_era'.</li> <li><strong>'dh_after_fab'</strong>: The elevation difference between the snow-free segment and FABDEM (ICESat-2 minus FABDEM), used in bias correction for FABDEM.</li> <li><strong>'snowdepth_fab'</strong>: The elevation difference between the snow-on segment and FABDEM, representing the uncorrected snow depth.</li> <li><strong>'sd_correct_fab'</strong>: The corrected snow depth using FABDEM, adjusted by bias correction model.</li> <li><strong>'df_fab_era5'</strong>: The difference between 'sd_correct_fab' and 'sde_era'.</li> </ol> <p>More explanation (especially on how the parameters are calculated, such as wind aspect index) is available in related works and blog posts on<a href="https://zhihaol.eu.org/blog/2023/subgrid/"> snow depth</a>, and <a href="https://zhihaol.eu.org/blog/2023/dataset/">DEM bias correction</a>.</p> <p>This dataset includes a comprehensive collection of snow depth data and correlated environmental variables for Mainland Norway. Researchers can use this dataset to investigate the following:</p> <ul> <li>The difference between ICESat-2 and DEMs. For example, how 'df_after_dtm1'<strong> </strong>relates to terrain parameters.</li> <li>The residual bias of ICESat-2 derived snow depth, for example, snowdepth_dtm1 and bias-corrected sd_correct_dtm1. You can train a better bias correction to retrieve snow depth again. You can compare your model with my model by 'dh_reg_dtm1', 'dh_reg_dtm10', 'dh_reg_cop30', and 'dh_reg_fab', which are the elevation differences after bias correction for each DEM.</li> <li>The difference between ICESat-2-derived snow depth and snow depth from ERA5 Land, for example, 'df_dtm1_era5'.</li> <li>The spatial distribution of snow depth or subgrid variability.</li> </ul>
Quality-checked horizontal particle flux data collected using a snow particle counter on board the R/V Akademik Tryoshnikov in the Southern Ocean during the austral summer of 2016/17 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Flux of particles (snow, rain and other particles including sea spray) were recorded passing through a photo-electric snow particle counter installed on board the R/V Akademik Tryoshnikov as part of the Antarctic Circumnavigation Expedition (ACE). Data were recorded from January to March 2017 in the Southern Ocean. Here we present the finalised, quality-checked, horizontal particle flux data where counts have been averaged over a one-minute period.</p> <p><strong>Dataset contents</strong></p> <ul> <li>SPC_HPF_windtrue_1min.csv, data file, comma-separated values</li> <li>SPC_HPF_windtrue_1min.png, metadata, portable network graphics</li> <li>SPC_HPF_windtrue_saveplot.py, script, Python code</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This quality-checked horizontal particle flux dataset from ACE 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/</p>
Intermediate processing stage of horizontal particle flux data collected using a snow particle counter on board the R/V Akademik Tryoshnikov in the Southern Ocean during the austral summer of 2016/17 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Flux of particles (snow, rain and other particles including sea spray) were recorded passing through a photo-electric snow particle counter installed on board the R/V Akademik Tryoshnikov as part of the Antarctic Circumnavigation Expedition (ACE). Data were recorded from January to March 2017 in the Southern Ocean. Here we present an intermediate step in data processing, with relative horizontal particle flux of particles with a size between 36 – 2000 μm averaged over one-minute periods. Data are presented in daily files.</p> <p><strong>Dataset contents</strong></p> <ul> <li>SPC_HPF_1min_YYYY_MM_DD.csv, data files, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged horizontal particle flux dataset from ACE 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/</p>
A Global Review of Long-range Transported Lead Concentration and Isotopic Ratio Records in Snow and Ice (Supplementary Data)
<p><strong>This is the supplemental material for:</strong></p> <p>Brooks, H.L., Miner, K.R., Kreutz, K.J., Winski, D.A., (in review). A Global Review of Long-range Transported Lead Concentration and Isotopic Ratio Records in Snow and Ice. </p> <p><strong>Purpose:</strong></p> <p>This systematic literature review contextualizes current data availability and examines spatial and temporal gaps in the long-range transported Pb analyses (concentration and isotope ratios) in ice and snow samples. Additionally, we note areas of needed community improvement. It is our hope that researchers will also benefit from a queryable set of references, allowing for quick access to the records appropriate to address multiple research questions. </p> <p><strong>Available Files:</strong></p> <p><em><strong>Table A1:</strong></em> Metadata for Pb records -- Individual sample sites</p> <p><em><strong>Table A2:</strong></em> Metadata for Pb records -- Transect sample sites</p> <p><em><strong>Table A3:</strong></em> Records grouped into 23 regions</p> <p><em><strong>Supplement_fig_25Aug2024: </strong></em>Additional figures supporting main manuscript</p> <p><em><strong>Supplement_method_25Aug2024: </strong></em>Methodology used for the systematic literature review</p> <p><em><strong>Supplement_citations_25Aug2024:</strong></em> Citations for all records included in the systematic literature review</p> <p><em><strong>citations_export.bib:</strong></em> Export of all systematic literature review citation data as bibtex format. Easy import to citation managers (Zotero, Mendley, Endnote, etc)</p> <p><em><strong>indexedReferences.csv:</strong></em> CSV dump of citations_export.bib indexed with citation keys used in TableA.3</p> <p><em><strong>tables.RDS: </strong></em>TableA.1, TableA.2, and indexed References formatted for easy import into R</p> <p><em><strong>tables.sqlite: </strong></em>TableA.1, TableA.2, and indexed References formatted for SQL queries in SQLite</p> <p><em><strong>readme_tables_sqlite.md:</strong></em> Examples of SQLite queries</p> <p> </p> <p><strong>Systematic Literature Review Methodology:</strong></p> <p>To address the current spatial and temporal distribution of long-range transported Pb deposited in the cryosphere (snow-pits and ice cores), we completed a systematic literature review, following the methodology outlined by Booth et al (2016). We completed an “exhaustive coverage [search], citing all relevant literature" (Booth et al., 2016), using the search terms “Lead (Pb) isotopes and concentration in surface snow, snow pits, and ice cores”. We performed an initial comprehensive literature search on these search terms on Web of Science Collection databases in September 2020 and May 2023. Records evaluated for relevance using the title and abstract. Removal of clearly off-topic papers (e.g., the chemistry of penguin feces) gathered in the search due to the dual meaning of “lead” reduced the paper count to 326 titles. The full text of the remaining publications was evaluated with clear explicit criteria for inclusion and exclusion, based on the following criteria.</p> <ol> <li> <ol> <li>Only studies examining long-traveled background atmospheric lead signals were considered. All point source pollution studies examining the localized effects of traffic, road salt, mines, industry, power plants, human activity at base camp stations, etc, were excluded. An exception was made for samples which were taken at sufficient depths in the analyzed record to predate the pollution source or where wind trajectory did not transport pollution to the collection site regardless of close geographic proximity.</li> <li> <p>Only studies of natural, undisturbed snowpacks and ice cores were examined. Studies which sampled snow from urban structures were excluded. Point source studies of emissions detail the localized effects of traffic, road salt, mines, industry, power plants, and human activity at base camp stations. While meaningful for understanding the direct emissions from various sources and developing new technology aimed at reducing source emissions, point source emission studies do not contribute to the understanding of regional and global signals. Additionally, studies examining the volcanic signal in snow following major modern eruptions were excluded, as this was classified as disturbed snow.</p> </li> <li>Studies must specify the sampling localities by providing a minimum of latitude and longitude. Where sampling locations are only referenced by colloquial names, the distance from point source pollution cannot be verified. Therefore, such studies were excluded.</li> <li> <p>Records of <sup>210</sup>Pb in snow and ice were excluded. <sup>210</sup>Pb is useful for establishing chronology in young snow and ice due to its small half life (~ 22.3 years). But it is not useful for consideration of old records and the source constraint of <sup>210</sup>Pb into the atmosphere is poorly constrained over time (Nijampurkar & Clausen, 1990). Therefore, it cannot be considered in conjunction with Pb isotopes and concentrations. Records of <sup>210</sup>Pb in snow and ice were excluded.</p> </li> <li> <p>Pb isotopes and concentrations taken from cryoconites (soil-like composites of dust, industrial soot, and microbial mats of photosynthetic bacteria) were excluded from this literature review. Cryoconites are important to glacial systems as they alter the albedo of the glacier surface, and therefore affect the glacier melt rate (Fountain et al., 2004). However, they must be considered separately from surface snow, snow pits, and ice cores due to the drastic differences in formation and biologic nature.</p> </li> <li> <p>The publication must be available to the author (<em>e.g.,</em> through the University Library, from collaborators)</p> </li> </ol> </li> </ol> <p>To ensure that the literature search conducted on the Web of Science was robust and complete, citations were checked to ensure inclusion in the literature search results and included when missing. Publications were indexed into Table A.1 and Table A.2. Following the completion of publication indexing, Table A.1 and Table A.2 were evaluated against the 23 regions (Table A.3) -- 20 from RGI 7.0 (RGI 7.0 Consortium, 2023) and 3 author defined regions -- to identify areas/papers that may have been missed in the initial search. Areas with few or no results were searched again using Google Scholar and Web of Science.</p> <p>Based on these searches, we sought to understand the current spatial and temporal coverage of these records, shed light on gaps in the previous research and make recommendations on mitigating these gaps going forward. We used tables and graphics, included in the main text and the supplement, to summarize the characteristics of the compiled records. In the main text, we discuss the limitations and gaps within the current long-range transported Pb literature, and recommend paths to mitigate these gaps. Finally, in the main text, we illustrate an example of how researchers can query this record compilation, allowing for quick access to the records appropriate to address their research questions.</p> <p><strong>Methodology Bibliography:</strong></p> <p>Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic approaches to a successful literature review (Second edition). Sage.</p> <p>Fountain, A. G., Tranter, M., Nylen, T. H., Lewis, K. J., & Mueller, D. R. (2004). Evolution of cryoconite holes and their contribution to meltwater runoff from glaciers in the McMurdo dry valleys, Antarctica. Journal of Glaciology, 50(168), 35–45. https://doi.org/10.3189/172756504781830312</p> <p>Nijampurkar, V. N., & Clausen, H. B. (1990). A century old record of lead-210 fallout on the greenland ice sheet. Tellus Series B Chemical and Physical Meteorology, 42(1), 29–38. https://doi.org/10.1034/j.1600-0889.1990.00005.</p> <p>RGI 7.0 Consortium. (2023). Randolph glacier inventory—A dataset of global glacier outlines, version 7.0. (Version 7.0) [Dataset]. NSIDC: National Snow and Ice Data Center. https://doi.org/doi:10.5067/f6jmovy5navz</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.