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1,604 results for “winter”
Soil temperature at GCE core monitoring sites in the winter of 2019-2020
We deployed one hobo logger in each vegetation zone at each of the ten primary GCE monitoring sites, for a total of 20 loggers. The loggers were deployed at plot number 1 in each zone, to the “outside” (away from plot number 2), parallel in elevation with the middle of plot 1, buried 10 cm deep, lying horizontal, and tied with a string to the upper left hand (looking from the ocean towards the land) corner stake of the plot. Hobos were deployed during fall monitoring in October 2019, and set to start logging on October 15, 1 am, at 15 minute intervals, with the loggers set on Central Time (times were converted to UTC in post-processing). They were retrieved in April 2020 and files were trimmed to end on a standard date. Exact deployment and retrieval dates are on the attached adobe acrobat file.
Annual Maps of Mean Winter Temperature for Eastern North America 1951-2009
We developed annual raster maps depicting spatiotemporal variation in mean winter temperature for the purposes of modeling the spread of the hemlock woolly adelgid. The maps are based on the PRISM and WorldClim datasets as described in methods.
North Temperate Lakes LTER Estimated winter inputs of stream water and groundwater to primary study lakes 1982 - 2014
This data set integrates and summarizes daily surface and groundwater inputs to 5 primary study lakes, using model estimates from a data-driven USGS hydrologic model (Hunt et al. 2013; Hunt and Walker 2017), and ice phenology data (number of days since ice-on). The lakes are Allequash, Big Muskellunge, Crystal, Sparkling, and Trout. Powers et al. (2017) used these data to estimate upper and lower bounds for exogenous chemical inputs to the lakes during winter. For a given lake and winter year, cumulative surface water and groundwater inputs were calculated across the ice cover period. For each lake, this data set reports the mean, maximum, and minimum winter water inputs observed across years, in units of water volume, % of average lake volume, and volume per winter day. Sampling Frequency: 1 per lake, with multiple summary values reported (i.e., mean, min, max). Number of sites: 5. Hunt, R.J. et al., 2013. Simulation of Climate - Change effects on streamflow, Lake water budgets, and stream temperature using GSFLOW and SNTEMP, Trout Lake Watershed, Wisconsin. USGS Scientific Investigations Report., pp.2013-5159. Available at: https://www.researchgate.net/publication/258363719_Simulation_of_Climate... Hunt, R.J., and Walker, J.F., 2017, GSFLOW groundwater-surface water model 2016 update for the Trout Lake Watershed, Wisconsin: U.S. Geological Survey data release, https://dx.doi.org/10.5066/F7M32SZ2. Powers SM, Labou SG, Baulch HM, Hunt RJ, Lottig NR, Hampton SE, Stanley EH. In press (expected 2017). Ice duration drives winter nitrate accumulation in north temperate lakes. Limnology and Oceanography Letters.
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
Circumpolar mid-winter thaw and refreeze based on fusion of Metop ASCAT and SMOS, 2011/2012 - 2021/2022
<p>Rain-on-Snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. Snow pack properties are changing and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event.</p> <p>Active and passive microwave data have been combined to identify events over land North of 65°N (Bartsch et al. 2023). In a first step Metop ASCAT (C-Band radar) was used to identify potential sudden snow structure change. In a second step, results have been masked for coincident observation of wet snow within +- 3 days based on SMOS (derived from Centre Aval de Traitement des Données SMOS (CATDS) level 3 product). Note that the SMOS retrievals can have data gaps due to radio frequency interferences (RFI) what leads to gaps in the event detection.</p> <p>The dataset is structured by centre points of the hexagonal grid of the used Metop ASCAT product (EUMETSAT, approximately 12.5 km nominal resolution). Attributes include point ID (GPI), latitude, longitude and</p> <ul> <li>aggregated number of events for the months November to February, 2011/12 to 2021/22, and their sum per winter (referred to as annual), or</li> <li>in case of daily results (date in file name) the magnitude of ASCAT backscatter change in dB (DSigma0; no data value is '0.0').</li> </ul> <p>The dataset extents Seawinds QuikScat (Ku-band) based results for 2000-2009 (Bartsch 2010, Freund and Bartsch 2020).</p>
Mid-winter habitat suitability indices for centrarchids in contiguous lentic areas of the Upper Mississippi River System: 1994-2018
This dataset includes raw measurements and calculated bluegill winter habitat suitability indices for depth (HSID), dissolved oxygen (HSIDO), temperature (HSIT), and flow (HSIF), as well as an overall bluegill winter habitat suitability index (HSIO), for 2915 mid-winter, lentic sampling locations across 208 contiguous lentic areas throughout the Upper Mississippi River System (Upper Mississippi and Illinois Rivers) from 1994-2018. This dataset also includes several spatial and temporal climatic and hydrogeomorphic parameters that were used to assess potential drivers of winter habitat suitability.
Greenhouse gas partial pressure (CO2, CH4, N2O) and environmental variables (physical, chemical, and biological) measured in urban ponds of Barcelona during summer and winter (2023-2024)
This dataset provides information on the partial pressure of greenhouse gases (CO₂, CH₄, and N₂O) measured in 41 artificial urban ponds—28 naturalized and 13 non-naturalized—using the headspace technique. Additionally, GPS coordinates, as well as physical, chemical, and biological variables for each pond, are included. Data were collected during the summer and winter seasons, during daytime. Furthermore, a subset of 16 ponds (8 naturalized and 8 non-naturalized) was also sampled at night in both seasons. All samples were taken from the water surface.
Winterberry: Fruit retention in fall and winter in 2016-2019 for four shrub species across Alaska
This dataset contains observations of fruit retention and state for Rosa acicularis (prickly rose), Empetrum nigrum (crowberry or blackberry), Vaccinium vitis-idaea (lowbush cranberry or lingonberry) and Viburnum edule (highbush cranberry). Data were collected at 47 sites in 25 communities in 6 ecoregions across Alaska, primarily by youth groups. Ecoregions include Bering taiga, Bering tundra, intermontane boreal, Alaska range transition, Aleutian meadows, and coastal rainforest. Observations were made approximately weekly during snow-free periods in fall and (at some sites) spring. At most sites only one species was monitored but some sites include observations on two species. Data consist of counts of unripe, ripe, rotten, dry, and damaged fruits. The dataset consists of one spreadsheet for each species and a file describing the location and habitat of each site.
CBC02 Winter-spring survival and response of birds to variable climate using mist-net captures at Konza Prairie
This dataset includes captures of small-bodied landbirds captured via passive mist-netting efforts. The objectives are to (a) initiate a long-term survey of the non-breeding birds of the site, (b) understand the behavioral and physiological mechanisms that allow birds to cope with the unpredictable, variable, and often harsh conditions during winter months, and (c) provide a training platform for students. The collection of this dataset is fully integrated into the teaching of “Wild Bird Research” (an undergraduate hands-on research course in the Division of Biology) and less formal instruction in bird research methods for graduate students. Additionally, the banding efforts have benefited from the engagement of Konza Prairie docents and frequently hosts class visits and other visitors interested in witness bird banding operations.
Storm Database Files for CLIMK–WINDS: A New Database of Extreme European Winter Windstorms
<p>This database is comprised of the four netCDF files containing the 50 most extreme European winter windstorms identified within the four input sources, with one netCDF file per source: ERA5 reanalysis, CCLM_ERA5_EUR-11 regional climate model simulation, COSMO-REA6 reanalysis, and CCLM_ERA5_CEU-3 regional climate model. This database was created by Clare Marie Flynn and its creation is described in the following paper: Flynn, C. M., Moemken, J., Pinto, J., Schutte, M., and Messori, G.: CLIMK–WINDS: A New Database of Extreme European Winter Windstorms, under review for final submission, Earth System Science Data, 2025.</p>
Raw data mzXML and MATLAB code for Variation in chemical composition of dissolved organic matter during the winter to spring transition in the northern Barents Sea
<p>MATLAB code and raw data mzXML for Variation in chemical composition of dissolved organic matter during the winter to spring transition in the northern Barents Sea.</p> <p>Seawater samples were collected during three distinct periods: early winter (December 2019), late winter (March 2021), and spring (May 2021). The sampling transect extended from the northern Barents Sea into the Nansen Basin (76°N – 83°N) as part of <em>The Nansen Legacy</em> project (Research Council of Norway, RCN #276730). The molecular composition of dissolved organic matter (DOM) was analyzed using an Orbitrap mass spectrometer.</p>
Data Package for the 2022 Great Lakes Winter Grab
WARNINGS: 1. For Ice Thickness data, please use data in "WinterGrab_snow_ice_properties" file instead of data in Table 2 of the manuscript published in Limnology and Oceanography Letters! 2: In "WinterGrab_phytoplankton_abundance_McKay", EC1 has two sets of data records because it was sampled both on 2/28 and 3/10, both records are included in this data. --- The data package contains the results from a multi-institutional winter limnology sampling campaign on the Laurentian Great Lakes. Researchers from 19 institutions sampled 49 locations in all five of the Great Lakes over a period of 24 days in February-March 2022. This dataset contains information on diverse physical, chemical, and biological parameters. Great Lakes Winter Grab ArcGIS Storymap showing all locations of sampling sites and select photos: https://storymaps.arcgis.com/stories/8ff1c332dd944ba9a744dc0e0fc18906
High-frequency winter water temperature and dissolved oxygen at Lake Sunapee, New Hampshire, USA, 2014-2023
The Lake Sunapee Protective Association (LSPA) has been monitoring water quality in Lake Sunapee, New Hampshire, USA, since the 1980s. Beginning in the winter of 2014-2015, the LSPA deployed a string of HOBO temperature sensors at a location near Loon Island (43.391N, 72.058W, where their instrumented buoy is located during the summer months) for under-ice water temperature profile monitoring. A HOBO U26 dissolved oxygen sensor was added to this monitoring string during the winter of 2017-2018 through the winter of 2019-2020. All sensors record data in 15-minute intervals over the winter and are downloaded after ice-off. All data have been QAQC'd to remove obviously errant readings and artifacts of maintenance and flag highly suspicious readings.
Variation in Landsat 8-estimated land surface temperature with elevation from Spartina alterniflora marsh cross sections in the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) site and Virginia Coast Reserve (VCR) LTER sites for winter and summer observations spanning 2013-2018
We estimated land surface temperature from top of atmosphere brightness temperature provided by Landsat 8's band 10 (a thermal band). We collected these measurements first for Spartina alterniflora dominated marsh near the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) eddy covariance flux tower. Measurements were collected from pixels along three east-west cross sections that spanned a marsh edge to interior gradient. We extracted Landsat 8 data for all available cloud-free low tide dates during August, September, January and February during the years 2013 to 2018 and associated these with marsh elevation information from a 1 m^2 Digital Elevation Model (DEM), created by Haldik et al 2013, also available from the GCE data catalog (http://dx.doi.org/10.6073/pasta/4c5187ef603f70cd0a77ece24ef0fed9). We rescaled the DEM to the coarser spatial resolution of Landsat 8 (30 x 30 m) where the rescaled elevation was the mean of the constituent DEM values. Ultimately, we used generalized additive models to relate land surface temperature to elevation, while accounting for variation from spatial proximity, transect and sample date. These models revealed that land surface temperature was negatively related to marsh elevation on the marsh platform. We then confirmed the generality of this pattern by rederiving these same relationships for three cross sections of Spartina alterniflora marsh at Virginia Coast Reserve (VCR) LTER for winter sampling dates only (data also included here). DEM data for VCR LTER are available at https://www.vcrlter.virginia.edu/gisdata/LIDAR/USGS2015/. We used custom R functions that can convert Landsat 8 top of atmosphere brightness temperature or top of atmosphere radiance from band 10 data to land surface temperature, which are available at https://github.com/jloconnell/convert_top_of_atmosphere_thermal_to_land_surface_temperature. Currently, a provisional land surface temperature product is available on earthexplorer.usgs.gov, w
Density and cover of winter annual plants in three harvester ant habitats at the Jornada Basin LTER site, 1987
This dataset contains plant cover and density data collected in three harvester ant (Pogonomyrmex rugosus) nesting habitats at the Jornada Basin LTER site in 1987. The purpose of this investigation was to answer three general questions: 1. How does the modification of soil properties and the ratios of resources (e.g., water-N) by ants alter species assemblages of winter annual plants at the edge of the ant nests? 2. How does the "spring cleaning", clipping, predation or herbivory by ants affect success of the winter annual plants at the edge of ant nests? 3. Are there significant differences in the floristic assemblage and belowground standing crop (root biomass) between the edge of ant nest and the surrounding unaffected soils? Variables included in the dataset include density and cover of all winter annual plants measured at regular intervals between January and May of 1987. Density is expressed as the number of individuals of a species per square meter. The cover of each species was calculated as the area covered by a perpendicular (not vertical) projection of its aerial parts onto the ground surface and expressed in covered area (cm squared) per square meter. This study was completed in 1987.
ICESat-2 monthly gridded winter Arctic sea ice thickness
<p>Monthly gridded (winter only) Arctic sea ice thickness estimates from ICESat-2 derived using ATL10 freeboards (https://nsidc.org/data/atl10) together with snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM, https://github.com/akpetty/NESOSIM). Along-track data (from the three strong beams) are binned to the 25 km x 25 km NSIDC polar stereographic projection (EPSG:3411). The full processing chain is described in Petty et al., (2020) (code available at https://github.com/akpetty/ICESat-2-sea-ice-thickness) including several updates as detailed below.</p> <p>Temporal range: November 2018 - April 2019, October 2019 to April 2020.</p> <p>Data: A single netCDF file is included for each month. Variables include:</p> <ul> <li>Sea ice freeboard (from ATL10)</li> <li>Snow depth (redistributed NESOSIM)</li> <li>Snow density (redistributed NESOSIM)</li> <li>Bulk sea ice density</li> <li>Sea ice type (from OSI SAF)</li> <li>Sea ice thickness uncertainty</li> <li>Mean day of month in a given grid cell</li> <li>Number of freeboard segments in a given grid cell.</li> </ul> <p>A summary of the differences between the version 1 and version 2 winter Arctic sea ice thickness estimates are being presented at AGU 2020 and prepared for publication.</p> <p>Key changes from version 1 (Petty et al., 2020) to version 2 include:</p> <ul> <li>Use of release 003 ATL10 freeboards. A detailed assessment of the freeboard changes from release 002 to release 003 is provided in Kwok et al., (2020).</li> <li>Upgrade to NESOSIM v1.1: CloudSat scaling of ERA5 snowfall, a new atmospheric wind loss term, calibration against recent OIB snow depths, an extended Arctic Ocean domain and various bug fixes (<a href="https://github.com/akpetty/NESOSIM">https://github.com/akpetty/NESOSIM</a>).</li> <li>Use of all three strong beams (instead of just strong beam #1).</li> </ul> <p>The data have also been made available on a Google Cloud bucket to enable rapid data analysis from any cloud-based analytics platform: <em>gs://sea-ice-thickness-data/v2/</em></p>
Unravelling winter diatom blooms in temperate lakes using high frequency data and ecological modeling
<p>This repository contains the dataset and the R script of the lake ecological model linked to the following publication:</p> <p>Article title: Unravelling winter diatom blooms in temperate lakes using high frequency data and ecological modeling</p> <p>Journal title: Water Research</p> <p>Article Number: 116681</p> <p>Abstract: In temperate lakes, it is generally assumed that light rather than temperature constrains phytoplankton growth in winter. Rapid winter warming and increasing observations of winter blooms warrant more investigation of these controls. We investigated the mechanisms regulating a massive winter diatom bloom in a temperate lake. High frequency data and process-based lake modeling demonstrated that phytoplankton growth in winter was dually controlled by light and temperature, rather than by light alone. Water temperature played a further indirect role in initiating the bloom through ice-thaw, which increased light exposure. The bloom was ultimately terminated by silicon limitation and sedimentation. These mechanisms differ from those typically responsible for spring diatom blooms and contributed to the high peak biomass. Our findings show that phytoplankton growth in winter is more sensitive to temperature, and consequently to climate change, than previously assumed. This has implications for nutrient cycling and seasonal succession of lake phytoplankton communities. The present study exemplifies the strength in integrating data analysis with different temporal resolutions and lake modeling. The new lake ecological model serves as an effective tool in analyzing and predicting winter phytoplankton dynamics for temperate lakes.</p>
Water vapor isotope data from Pallas-Yllastunturi National Park, Finland (Winter 2017-18)
<p>Calibrated water vapor isotope and mixing ratio data from Pallas-Yllastunturi National Park, Finland.</p> <p>Site Name: Sammaltunturi Station, Finland (Finnish Meteorological Institute) <br> Site Location: 67.973°N; 24.116°E <br> Site Elevation: 565 m above sea level <br> Instrumentation: Picarro L2130-i Isotope and Gas Concentration Analyser<br> Parameters: δ<sup>18</sup>O water vapor, δ<sup>2</sup>H water vapor, deuterium (d)-excess water vapor, mixing ratio (5-minute averages)<br> Date/Time start: 20/12/2017 05:45 EET<br> Date/Time end: 31/03/2018 23:55 EET</p>
Jensen et al. 2024 - Biodiversity and distribution of gelatinous macrozooplankton in the North Sea and adjacent waters dataset from winter 2022 - raw dataset
<p><span>The diversity and distribution of gelatinous macrozooplankton is described by presenting qualitative and quantitative data of the jellyfish and comb jelly community encountered in the North Sea and Skagerrak/Kattegat during January/February 2022.<span> </span>Data were generated<span> </span>as part of the North Sea Midwater Ring Net survey (MIK), an ichthyoplankton survey conducted at night-time during the quarter 1 (Q1) International Bottom Trawl Survey (IBTS), aboard the Danish R/V DANA (DTU Aqua) and the Swedish R/V Svea (SLU) at a total of 100 stations. This dataset accompanies the Data in Brief Article below and should be cited when using this dataset. <br></span></p> <p><span>Jensen, C.J.D., Køhler, L.G., Huwer, B., Werner, M., Cieters, L., <strong>Jaspers, C.</strong> (submitted) Biod</span><span>iversity and distribution of gelatinous macrozooplankton in the North Sea and adjacent waters dataset from winter 2022. <em>Data in Brief. </em></span></p>
Soil and meteorological data, and finite element simulation framework for heat transfer through shrubs in winter near Lautaret pass, French Alps
<p>The data allow the calculation using finite element modeling of heat transfer through shrub branches and snow between the atmosphere and the soil. The shrubs are green alders (Alnus viridis). The site where they are found is called Alnus-Nivus (45.034750°N, 6.413630°E, 2034 m asl) near Col du Lautaret, French Alps. The soil data consist in temperature and volumetric liquid water content at 5 and 15 cm depths. One spot is near the alder collar (ALNUS), the other spot is 6 m away, under grass (GRASS).</p> <p>The meteorological data were obtained from the FR-Clt station, 750 m away (45.041278°N, 6.410611°E, 2046 m asl). See (Gupta et al., 2023) for details. Only the data relevant for heat transfer simulations are given.</p> <p>The simulation framework gives the alder mesh used in the heat transfer simulations. Typical simulations use a wood thermal conductivity of 1 W m<sup>-1</sup> K<sup>-1</sup> and a snow thermal conductivity of 0.1 W m<sup>-1</sup> K<sup>-1</sup>. Based on observations, the snow height at Alnus-Nivus is likely to be at least twice the value at FR-Clt. Forcing uses the snow surface temperature, derived from upwelling longwave radiation using an emissivity of 1. The data allow testing thermal bridging through shrub branches. These data are used in a publication in preparation: Domine, Fourteau, Choler, Exploration of Thermal Bridging Through Shrub Branches in Alpine Snow.</p> <p>Reference</p> <p>Gupta, A., Reverdy, A., Cohard, J. M., Hector, B., Descloitres, M., Vandervaere, J. P., Coulaud, C., Biron, R., Liger, L., Maxwell, R., Valay, J. G., and Voisin, D.: Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment, Hydrol. Earth Syst. Sci., 27, 191-212, 2023.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.