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45 results for “Arctic Observing”
Phenological stages of deciduous plants were observed at a long term experimental moist acidic tussock tundra site, Arctic LTER 1996 Toolik Lake, AK.
Phenological stages of deciduous plants were observed at a long term experimental moist acidic tussock tundra site (Arctic LTER) near Toolik Field Station, AK. Also, ITEX maximum growth measurements were recorded on August 19th (moist tussock tundra). Experimental treatments at each site included factorial NxP, greenhouse and shadehouse and were begun in 1989. See 96gspheg.html and 96gsphsg. html for phenological data on evergreen and sedge species.
Phenological stages of evergeen plants were observed at a long term experimental moist tussock tundra site (Arctic LTER) 1996 near Toolik Lake, AK.
Phenological stages of evergeen plants were observed at a long term experimental moist acidic tussock tundra (Arctic LTER) in 1996 near Toolik Lake, AK. Also, ITEX maximum growth measurements were recorded on August 19th (moist tussock tundra). Experimental treatments at each site included factorial NxP, greenhouse and shadehouse and were begun in 1989. See 96gsphdc and 96gsphsg for phenological data on deciduous and sedge species.
Phenological stages of sedges were observed at a long term experimental moist tussock tundra site and a long-term experimental wet sedge tundra sites (Arctic LTER) for 1996 near Toolik Lake, AK.
Phenological stages of sedges were observed at a long term experimental moist tussock tundra site and a long-term experimental wet sedge tundra sites near Toolik Lake, AK. Also, ITEX maximum growth measurements were recorded on August 19th (moist tussock tundra). Experimental treatments at each site included factorial NxP, greenhouse and shadehouse and were begun in 1989. See 96gsphdc.html and 96gsphsg.html for phenological data on deciduous and evergeen species.
Processed model output and observational products used in `Observed winds crucial for September Arctic sea ice loss'
<p>Processed model output from wind-nudging experiments used to investigate Arctic sea ice loss. Also includes processed observational data shown in the manuscript.</p> <p> </p> <p>For further details, see </p> <p>Roach, L. A and Blanchard-Wrigglesworth E. (2022). Observed winds crucial for September Arctic sea ice loss. Accepted at Geophysical Research Letters</p>
Model and observations used for Sigmond, M. et al, 'Large contribution of ozone-depleting substances to global and Arctic warming in the late 20th century", Geophysical Research Letters
<p>This dataset contains all the data used for the study "Large contribution of ozone-depleting substances to global and Arctic warming in the late 20th Century" by Sigmond et al, which was submitted to Geophysical Research Letters on July 26, 2022. It contains the following directories:</p> <p>- AR6: csv data files containing timeseries of Effective Radiative Forcing (ERF) as assessed by the IPCC Sixth Assessment Report (AR6).</p> <p>- CanESM5/DATA_siconc_canesm5_past_[expname] contain timeseries of September Sea Ice Extent (SSIE), where expname=historical (all forcing) or hist-noXXX (as historical, except that XXX is fixed to 1955 values)</p> <p>- CanESM5/DATA_tas_canesm5_past_[expname] contains timeseries of surface air temperature (am_: annual mean, arcam_: Arctic and annual mean, gmam_: Global and annual mean). expname as above </p> <p>- CanESM5/IN_TOA_COUPLED_CanESM5 contain timeseries of the global mean top of atmosphere net raditative flux. his=historical (all forcing) run, rms0056=fixed CFC run, rms0058=fixed CFC+stratospheric ozone run, rms0065: fixed CO2 run, and rms0066=fixed aerosol run.</p> <p>- CanESM5_uncoupled/DATA_rtmt_canesm5_YYYY: contains timeseries of RTMT (top of atmosphere radiative imbalance) in the uncoupled (fixed 1955 SST/SI) CanESM5 simulations. YYYY=fall: historical, faer: fixed aerosols, fco2: fixed CO2, fods: fixed CFC runs</p> <p>- OBSERVATIONS: contains timeseries of observational September Sea Ice Extent and surface air temperature used in the study</p> <p>- data_GSAT+ASAT+ERF+efficacies_2001-2005min1955.txt: contains the most import post-processed data </p> <p>Python code used to create these files from the raw model output are available at https://gitlab.com/michael.sigmond/cfc/</p>
The prediction data analyzed in "Seasonal Arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations"
<p>The outputs of seasonal predictions with the new modeling system analyzed in the article including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Near surface air temperature (T2) </p>
Dataset for "Spaceborne observations of lightning NO2 in the Arctic"
<p>Core data used in <a href="https://github.com/zxdawn/S5P-LNO2-Notebook">S5P-LNO2-Notebook</a> repository for <a href="https://doi.org/10.1021/acs.est.2c07988">Zhang et al. (2023)</a>.</p> <p>For the full S5P LNO2 product (June-August, 2019-2021), please check the Arctic lightning NO2 product (<a href="https://doi.org/10.5281/zenodo.7547817">2019</a>, <a href="https://doi.org/10.5281/zenodo.7547819">2020</a>, <a href="https://doi.org/10.5281/zenodo.7547825">2021</a>).</p> <p><strong>Reference</strong></p> <p>Zhang et al., <strong>Spaceborne observations of lightning NO<sub>2</sub> in the Arctic</strong>, <em>Environ. Sci. Technol</em>.</p> <p><strong>Date files</strong></p> <ul> <li> <p>CAMS</p> <ul> <li> <p>CAMS anthropogenic, ship, and soil NOx emissions (2018)</p> </li> </ul> </li> <li> <p>clean lightning</p> <ul> <li> <p>Two clean lightning cases of S5P LNO2 product.</p> <p>Because it is ~1G per file, I just uploaded two files.</p> </li> </ul> </li> <li> <p>era5</p> <ul> <li> <p>Monthly CAPE (2019-2021 summer)</p> </li> </ul> </li> <li> <p>GFAS</p> <ul> <li> <p>GFAS wildfire NOx emission (2018-2021)</p> </li> </ul> </li> <li> <p>gld360</p> <ul> <li> <p>See <a href="https://doi.org/10.5281/zenodo.7528016">10.5281/zenodo.7528016</a> (need request)</p> </li> </ul> </li> <li> <p>lno2</p> <ul> <li> <p>Lightning NO2 emission product</p> <ul> <li> <p>LNO2_emiss.nc</p> </li> <li> <p>LNO2_emiss_area.nc</p> </li> </ul> </li> <li> <p>LNOx profile of Luo et al. 2016</p> </li> <li> <p>Gridded 0.1 x 0.1 LNO2 product</p> <ul> <li> <p>S5P_LNO2_grid.nc</p> <p>vars: lno2, lno2_max, lno2_sum, lightning, and lightning_500hpa</p> </li> </ul> <ul> <li> <p>S5P_LNO2_grid_product.nc</p> <p>vars: no2, lno2, lightning_counts, and cloud_pressure_crb</p> </li> </ul> </li> <li> <p>LNO2 lifetime products</p> <ul> <li> <p>S5P_LNO2_lifetime.csv</p> </li> <li> <p>S5P_LNO2_lifetime.nc (without lightning data, need request for original data)</p> </li> </ul> </li> <li> <p>LNO2 production products</p> <ul> <li> <p>S5P_LNO2_production.csv</p> </li> <li> <p>S5P_LNO2_production.nc (without lightning data, need request for original data)</p> </li> <li> <p>S5P_LNO2_production_**.csv (sensitivity tests)</p> </li> </ul> </li> <li> <p>Lightning within TROPOMI swath</p> <ul> <li> <p>swath_lightning_**.csv (need request)</p> </li> </ul> </li> </ul> </li> <li> <p>merra2</p> <ul> <li> <p>MERRA2 AOD netcdf files</p> </li> </ul> </li> <li> <p>otd</p> <ul> <li> <p>OTD low resolution and high resolution monthly data</p> </li> </ul> </li> <li> <p>tropomi</p> <ul> <li> <p>Web scrapied S5P-PAL TROPOMI NO2 L2 swath shapes</p> </li> </ul> </li> <li> <p>tropomi_regrid_combine</p> <ul> <li> <p>Regridded summertime TROPOMI NO2 L2 data</p> </li> </ul> </li> <li> <p>viirs</p> <ul> <li> <p>VIIRS fire archive csv data</p> </li> </ul> </li> </ul>
Data for "Observed winter Barents Kara Sea ice variations induce prominent sub-decadal variability and a multidecadal trend in the Warm Arctic Cold Eurasia pattern"
<p>Here the model experiment data used to create the figures in the article "Observed winter Barents Kara Sea ice variations induce prominent sub-decadal variability and a multi-decadal trend in the Warm Arctic Cold Eurasia pattern" are provided.</p>
Observation Database for Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system
<p>Basic matplotlib visualization for observational databases.<br>Archive files, readme, and visualisation script are attached.</p> <p>The python script visualizes the statistics related to observation <br>minus background departures for AMSU-A and MHS sensors using<br>the default and footprint observation operators in the AROME-Arctic data assimilation system.</p> <p>The corresponding preprint:<br>gmd-2023-195 | Submitted 02 Oct 2023 | Development and technical paper<br>Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system<br>Máté Mile, Stephanie Guedj, and Roger Randriamampianina </p>
Model data and namelists for Sterzinger et al. (2022) - "Do arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations"
<p>Model data and namelists for "<a href="https://acp.copernicus.org/preprints/acp-2022-36/">Do arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations</a>"</p> <p>Horizontally averaged data is provided in NetCDF4 files (oliktok.nc, ascos.nc, summit.nc) for all output variables. Horizontally averaged vertical momentum flux is provided in a separate file for each simulation (*_vert_momentum_flux.nc files).</p> <p>Info on variables is provided by the RAMS model variable guide PDF <a href="https://vandenheever.atmos.colostate.edu/vdhpage/rams/docs/RAMS-VariableList.pdf">available here</a>.</p> <p>Model namelists are provided for each simulation (*_RAMSIN files). ASCOS initialization sounding info is provided within the ASCOS_RAMSIN file - initialization soundings are provided in SOUND_IN files.</p>
data of " Arctic sea fog observation along trans-Arctic shipping routes"
<p>2025-03-08 Erratum for the 【README.txt】 file : The ASOS station data in the 【ASOS Arctic sites.zip】 file covers the period from 1971 to 2023. However, in the article 'Observed Climatology and Formation Mechanisms of Sea Fog Along the Trans-Arctic Shipping Routes', the study timeframe was selected as 1979–2023 to account for data quality considerations and to facilitate cross-dataset comparisons.</p>
Data from: Effects of distance on detectability of Arctic waterfowl using double-observer sampling during helicopter surveys
Aerial survey is an important, widely employed approach for estimating free‐ranging wildlife over large or inaccessible study areas. We studied how a distance covariate influenced probability of double‐observer detections for birds counted during a helicopter survey in Canada's central Arctic. Two observers, one behind the other but visually obscured from each other, counted birds in an incompletely shared field of view to a distance of 200 m. Each observer assigned detections to one of five 40‐m distance bins, guided by semi‐transparent marks on aircraft windows. Detections were recorded with distance bin, taxonomic group, wing‐flapping behavior, and group size. We compared two general model‐based estimation approaches pertinent to sampling wildlife under such situations. One was based on double‐observer methods without distance information, that provide sampling analogous to that required for mark–recapture (MR) estimation of detection probability, urn:x-wiley:20457758:media:ece34824:ece34824-math-0001, and group abundance, urn:x-wiley:20457758:media:ece34824:ece34824-math-0002, along a fixed‐width strip transect. The other method incorporated double‐observer MR with a categorical distance covariate (MRD). A priori, we were concerned that estimators from MR models were compromised by heterogeneity in urn:x-wiley:20457758:media:ece34824:ece34824-math-0003 due to un‐modeled distance information; that is, more distant birds are less likely to be detected by both observers, with the predicted effect that urn:x-wiley:20457758:media:ece34824:ece34824-math-0004 would be biased high, and urn:x-wiley:20457758:media:ece34824:ece34824-math-0005 biased low. We found that, despite increased complexity, MRD models (ΔAICc range: 0–16) fit data far better than MR models (ΔAICc range: 204–258). However, contrary to expectation, the more naïve MR estimators of urn:x-wiley:20457758:media:ece34824:ece34824-math-0006 were biased low in all cases, but only by 2%–5% in most cases. We suspect that this apparently anomalous finding was the result of specific limitations to, and trade‐offs in, visibility by observers on the survey platform used. While MR models provided acceptable point estimates of group abundance, their far higher stranded errors (0%–40%) compared to MRD estimates would compromise ability to detect temporal or spatial differences in abundance. Given improved precision of MRD models relative to MR models, and the possibility of bias when using MR methods from other survey platforms, we recommend avian ecologists use MRD protocols and estimation procedures when surveying Arctic bird populations.
Variability of Atmospheric CO2 Over the Arctic Ocean: Insights From the O-Buoy Chemical Observing Network
<p>This dataset contains relevant files for the GEOS-Chem chemical transport model simulations used in the following manuscript: Graham, K. A., Friedrich, G., Rauschenberg, C. D., Williams, C. R., Bottenheim, J. W., Chavez, F. P., Halfacre, J. W., Holmes, C. D., Perovich, D. K., Shepson, P. B., Simpson, W. R., Tans, P. P., & Matrai, P. A. (2022). Variability of Atmospheric CO<sub>2</sub> Over the Arctic Ocean. <em>Journal of Geophysical Research: Atmospheres</em>. In Review.</p>
Data from: Spatial variation and linkages of soil and vegetation in the Siberian Arctic tundra – coupling field observations with remote sensing data
Open the record for dataset details and reuse information.
Data from: Effects of distance on detectability of Arctic waterfowl using double-observer sampling during helicopter surveys
Open the record for dataset details and reuse information.
Comparison Metrics for ERA5 against in situ observations in the Arctic
<p>The comparison metrics (R2, slope, RMSE, Pearson correlation coefficient) were produced from a comparison between the ERA5 reanalysis model and in situ observations from ground-based stations in the Arctic. The datasets accompany the manuscript "Comparison of selected surface level ERA5 variables against in situ observations in the Arctic" by Pernov et al. (2023). The Excel files are located in three sub-folders (All, Seasonal, and Temporal). Each variable has its own Excel file. The sub-folder "All" gives the metrics when using all available data from each station at a 1-hour temporal resolution. The sub-folder "Seasonal" gives the metrics when the comparison is made on a seasonal basis, with sub-folders indicating the season. The sub-folder "Temporal" gives the metrics when comparing different temporal resolutions, which are indicated in the sub-folders. </p> <p>For questions, please contact jakob.pernov@epfl.ch or julia.schmale@epfl.ch</p>
Fig. 3 in On Papulifères, putative ciliate cysts of diverse morphologies, with new observations from the plankton of the Chukchi Sea (Arctic Ocean)
Fig. 3. The illustrations of the variety of forms, all described as Fusopsis, from Kufferath (1950) that he found in plankton samples from the southern North Sea and the English Channel (NW Europe). The original illustrations shown here as A to F appear in Kufferath's report, respectively, as 16, 17, 20, 18, 19, and 21.
Fig. 1 in On Papulifères, putative ciliate cysts of diverse morphologies, with new observations from the plankton of the Chukchi Sea (Arctic Ocean)
Fig. 1. Meunier's Papulifères from Meunier 1910, 1919. A–I: Fusopsis forms; J–M: Piropsis forms; N–Y: Sphaeropsis forms. Meunier's names for each, approximate sizes as he depicted them (figures above were re-sized to fit into a single plate), and locations in his reports are given in Table 1.
Historical Arctic and Antarctic Surface Observational Data, Version 1
This product consists of meteorological data from 105 Arctic weather stations and 137 Antarctic stations, extracted from the National Climatic Data Center (NCDC)'s Integrated Surface Hourly (ISH) database. Variables include wind direction, wind speed, visibility, air temperature, dew point temperature, and sea level pressure. Temporal coverage varies by station, with the earliest record in 1913 and the latest in 2002. Data are in tab-delimited ASCII text format, with one file per station and year. Graphs of meteorological variables throughout the time series accompany the ASCII data.
Dendrometer, Soil, and Weather Observations, Arctic Tree Line, AK and NWT, 2016-2019
This dataset provides in situ measurements of radial tree growth of selected white spruce (Picea glauca) and black spruce (Picea mariana) trees, as well as simultaneous in situ measurements of environmental variables (air temperature, air pressure, relative humidity, soil temperature, volumetric water content, and solar irradiance) at two Arctic treeline sites: one in the Brooks Range of Alaska (AK), USA, and the other near Inuvik, Northwest Territories (NWT), Canada. In AK, 36 trees were monitored from June 7, 2016 to September 13, 2019, and in NWT, 24 trees were monitored from July 5, 2017 to July 25, 2019 with a sampling interval of 5- or 20-minutes for radial tree growth and 5-minutes for all environmental variables. The dendrometer data included in this dataset are only those gathered from 2016-2017. Dendrometer data from 2018-2019 are available from a related dataset. The data were collected to better understand the influence of environmental variables on radial tree growth dynamics. The data are provided in comma-separated values (CSV) format.
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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.