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32 results for “Snowfall”
MASS2ANT Snowfall Dataset (Downscaling @5.5km over Dronning Maud Land, Antarctica, 1850 - 2014)
<p><strong>MASS2ANT Snowfall Dataset: Information for users</strong></p> <p>November 2020</p> <p> </p> <p>N. Ghilain<sup>1</sup>, S. Vannitsem<sup>1</sup>, Q. Dalaiden<sup>2</sup>, H. Goosse<sup>2</sup>, L. De Cruz<sup>1</sup></p> <p><sup>1</sup>Royal Meteorological Institute, Uccle, Belgium</p> <p><sup>2</sup>UCLouvain, Earth and Life Institute, Louvain-la-Neuve, Belgium</p> <p> </p> <p>contacts RMI: <a href="mailto:stephane.vannitsem@meteo.be">stephane.vannitsem@meteo.be</a>, <a href="mailto:nicolas.ghilain@meteo.be">nicolas.ghilain@meteo.be</a>, <a href="mailto:lesley.decruz@meteo.be">lesley.decruz@meteo.be</a></p> <p>contacts UCLouvain: <a href="mailto:hugues.goosse@uclouvain.be">hugues.goosse@uclouvain.be</a>, <a href="mailto:quentin.dalaiden@uclouvain.be">quentin.dalaiden@uclouvain.be</a></p> <p> </p> <p>We provide in this dataset maps at 5.5 km resolution of the daily and yearly accumulated snowfall over emerged land (Ice Sheet) of Dronning Maud Land (Antarctica) from 1850 to 2014. We used a statistical method to derive fine resolution maps from GCM runs (CESM2, 10 runs). In the method, we searched for analogs in a database we constructed from the association between re-analyses large-scale meteorological fields (ERA5 and ERA-Interim) and RCM daily accumulated snowfall (RACMO2.3p5.5). RACMO2.3p5.5 data are available freely on request (<a href="https://www.projects.science.uu.nl/iceclimate/models/antarctica.php">https://www.projects.science.uu.nl/iceclimate/models/antarctica.php</a>). CESM2 CMIP6 runs are also freely available (<a href="https://esgf-node.llnl.gov/search/cmip6/">https://esgf-node.llnl.gov/search/cmip6/</a>). The complete description of the algorithm and performance is described in: <em>Ghilain </em><em>N.,</em><em> Vannitsem </em><em>S.,</em><em> Dalaiden </em><em>Q.,</em><em> Goosse </em><em>H.,</em><em> De Cruz </em><em>L.,</em><em> </em><em>Wei</em><em> </em><em>W., </em><em>Reconstruction</em><em> of d</em><em>aily snowfall accumulation at 5.</em><em>5</em><em>km resolution over Dronning Maud Land, Antarctica, from 1850 to 2014 </em><em>using an analog-based downscaling technique</em>, submitted to Earth System Science Data (ESSD).</p> <p> </p> <p>The MASS2ANT Snowfall dataset is composed of the annual estimations of snowfall over Dronning Maud Land, the daily time series for the total period for all the emerged grid points of the domain (2 files are given in example, the all set is available on zenodo: 10.5281/zenodo.6355455, 10.5281/zenodo.6359385, 10.5281/zenodo.6362299), the principal components time series and Empirical Orthogonal Functions (EOF) offering the possibility to analyze the synoptic weather patterns associated to snowfall over the ice sheet and the Principal Component weights (PCs) time series from the re-analysis in case one wants to extend or improve the database. Realistic weather patterns can be recomposed in associating (product of matrices) the PCs with the EOFs</p> <p> </p> <p>The method could be easily expanded to other sources (other GCM, other reference RCM or other reanalysis), and potentially for other parts of Antarctica. Samples of the programs used to generate this database are therefore also provided here.</p> <p>Refer to <a href="https://zenodo.org/api/files/d8e358fa-f3d7-457e-949b-94f323ff04f9/MASS2ANT_Snowfall_Dataset_InfoUsers.pdf">MASS2ANT_Snowfall_Dataset_InfoUsers.pdf</a></p> <p> </p>
Dataset for Heavy snowfall event over the Swiss Alps: Did wind shear impact secondary ice production?
<p>The change in wind direction and speed with height, referred to as vertical wind shear, causes enhanced turbulence in the atmosphere. As a result, there are enhanced interactions between ice particles that break up during collisions in clouds which could cause heavy snowfall. For example, intense dual-polarization Doppler signatures in conjunction with strong vertical wind shear were observed by an X-band weather radar during a wintertime high-intensity precipitation event over the Swiss Alps. An enhancement of differential phase shift (Kdp > 1◦ km−1) around −15◦C suggested that a large population of oblate ice particles was present in the atmosphere. Here, we show that ice–graupel collisions are a likely origin of this population, probably enhanced by turbulence. We perform sensitivity simulations that include ice–graupel collisions of a cold frontal passage to investigate whether these simulations can capture the event better and whether the vertical wind shear had an impact on the secondary ice production (SIP) rate. The simulations are conducted with the Consortium for Small-scale Modeling (COSMO), at a 1km horizontal grid spacing in the Davos region in Switzerland. The rime splintering simulations could not reproduce the high ice crystal number concentrations, produced too large ice particles and therefore overestimated the radar reflectivity. The collisional-breakup simulations reproduced both the measured horizontal reflectivity and the ground-based observations of hydrometeor number concentration more accurately (∼ 20L−1). During 14:30–15:45UTC, the vertical wind shear strengthened by 60% within the region favorable for SIP. Calculation of the mutual information between the SIP rate and vertical wind shear and updraft velocity suggests that the SIP rate is best predicted by the vertical wind shear rather than the updraft velocity. The ice–graupel simulations were insensitive to the parameters in the model that control the size threshold for the conversion from ice to graupel and snow to graupel.</p>
Machine Learning Snowfall LPM
<p>This dataset contains a 65-year snowfall climatology for stations in Lower Michigan Penisular and associated environment variables.</p> <p>We also have several R codes examples for the machine learning model training.</p>
Snow cover in the European Alps: Station observations of snow depth and depth of snowfall
<p>Auxiliary files, code, and data for paper published in The Cryosphere:</p> <p>Observed snow depth trends in the European Alps 1971 to 2019</p> <p> <a href="https://doi.org/10.5194/tc-15-1343-2021">https://doi.org/10.5194/tc-15-1343-2021</a></p> <p> </p> <p><strong>Auxiliary files:</strong></p> <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> <p> </p> <p><strong>Code </strong>(working copy, not cleaned, all written in R statistical software): code.zip</p> <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> <p> </p> <p><strong>Data</strong>:</p> <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 <strong>use the data you agree to adhere to the respective data provider's terms</strong> 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: <strong>Attribution</strong> — 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> <p> </p> <p> </p> <p><strong>Version history:</strong></p> <p>v1.3: added maxHS and SCD (with various 1-100cm thresholds) to monthly data</p> <p>v1.2: uploaded data</p> <p>v1.1: changes to aux-paper.zip and code.zip as consequence from submitting a revised manuscript</p> <p>v1.0: initial upload</p>
Supplementary data: What millimeter-wavelength radar reflectivity reveals about snowfall: An information-centric analysis
<p>This dataset includes supplementary data used in the analyses described in Wood, N. B., and T. S. L'Ecuyer, 2020: What millimeter-wavelength radar reflectivity reveals about snowfall: An information-centric analysis. Atmospheric Measurement Techniques, doi:10.5194/amt-2020-216.</p>
Global Atlas of Fraction of Snowfall in Forest
<p><strong>Global Atlas of Fraction of Snowfall in Forest (FSF)</strong></p> <p>The FSF (FSF_11km.tif) is the product of the fraction of snowfall (Snowfall_Fraction*) from ERA5-Land (Muñoz Sabater et al., 2019) with the tree cover (TreeCover_11km.tif) from Hansen et al. (2013).</p> <p>Digital elevation model (DEM_11km.tif) from the Copernicus GLO30 product and Köppen-Geiger climate (Koppen_11km.tif) are also provided on the same grid as FSF.</p> <p>The FSF is also averaged per hydrological basin of level 3 in the HydroBasin dataset (Lehner et al., 2013) and mountain range (Snethlage et al., 2022) respectively in data_hybas.geojson and data_gmba.geojson.</p> <p> </p> <p><strong>References</strong></p> <p>Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., Townshend, J. R. G. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. <em>Science</em>, <em>342</em>(6160), 850–853. https://doi.org/10.1126/science.1244693</p> <p> </p> <p>Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World Map of the Köppen-Geiger climate classification updated. <em>Meteorologische Zeitschrift</em>, <em>15</em>(3), 259–263. https://doi.org/10.1127/0941-2948/2006/0130</p> <p> </p> <p>Lehner, B., & Grill, G. (2013). Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. <em>Hydrological Processes</em>, <em>27</em>(15), 2171–2186. https://doi.org/10.1002/hyp.9740</p> <p> </p> <p>Muñoz Sabater, J. (2019). ERA5-Land monthly averaged data from 1981 to present [Data set]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/doi:10.24381/cds.68d2bb30</p> <p> </p> <p>Snethlage, Mark A., Geschke, J., Ranipeta, A., Jetz, W., Yoccoz, N. G., Körner, C., et al. (2022). A hierarchical inventory of the world’s mountains for global comparative mountain science. <em>Scientific Data</em>, <em>9</em>(1), 149. https://doi.org/10.1038/s41597-022-01256-y</p>
Data for the publication "Too frequent and too light Arctic snowfall with incorrect precipitation phase partitioning in the MIROC6 GCM"
<p>These data are a set of 1yr simulations using the MIROC6-SPRINTARS global aerosol-climate model with different treatments of precipitation (i.e., diagnostic and prognostic). The outputs include diagnostics from the satellite simulator COSP2.<br> The data are used in the manuscript entitled "Too frequent and too light Arctic snowfall with incorrect precipitation phase partitioning in the MIROC6 GCM". All data used in this study are available from the corresponding author upon request.</p>
MASS2ANT Snowfall Dataset (Downscaling @5.5km over Dronning Maud Land, Antarctica, 1850 - 2014): Daily fields (Part 1)
<p>We provide in this dataset maps at 5.5 km resolution of the daily and yearly accumulated snowfall over emerged land (Ice Sheet) of Dronning Maud Land (Antarctica) from 1850 to 2014. We used a statistical method to derive fine resolution maps from GCM runs (CESM2, 10 runs). In the method, we searched for analogs in a database we constructed from the association between re-analyses large-scale meteorological fields (ERA5 and ERA-Interim) and RCM daily accumulated snowfall (RACMO2.3p5.5). RACMO2.3p5.5 data are available freely on request (<a href="https://www.projects.science.uu.nl/iceclimate/models/antarctica.php">https://www.projects.science.uu.nl/iceclimate/models/antarctica.php</a>). CESM2 CMIP6 runs are also freely available (<a href="https://esgf-node.llnl.gov/search/cmip6/">https://esgf-node.llnl.gov/search/cmip6/</a>). The complete description of the algorithm and performance is described in: <strong><em>Ghilain </em><em>N.,</em><em> Vannitsem </em><em>S.,</em><em> Dalaiden </em><em>Q.,</em><em> Goosse </em><em>H.,</em><em> De Cruz </em><em>L.,</em><em> </em><em>Wei</em><em> </em><em>W., </em><em>Reconstruction</em><em> of d</em><em>aily snowfall accumulation at 5.</em><em>5</em><em>km resolution over Dronning Maud Land, Antarctica, from 1850 to 2014 </em><em>using an analog-based downscaling technique</em></strong>, submitted to Earth System Science Data (ESSD).</p> <p>The MASS2ANT Snowfall dataset is composed of the annual estimations of snowfall over Dronning Maud Land, the daily time series for the total period for all the emerged grid points of the domain, the principal components time series and Empirical Orthogonal Functions (EOF) offering the possibility to analyze the synoptic weather patterns associated to snowfall over the ice sheet and the Principal Component weights (PCs) time series from the re-analysis in case one wants to extend or improve the database. Realistic weather patterns can be recomposed in associating (product of matrices) the PCs with the EOFs.</p> <p>Here (Daily fields - Part 1), we provide the daily snowfall time series resulting from the downscaling of the 7 first members of CESM2, using ERA5 and RACMO2.3p5.5 for training.</p>
MASS2ANT Snowfall Dataset (Downscaling @5.5km over Dronning Maud Land, Antarctica, 1850 - 2014): Daily fields (Part 3)
<p>We provide in this dataset maps at 5.5 km resolution of the daily and yearly accumulated snowfall over emerged land (Ice Sheet) of Dronning Maud Land (Antarctica) from 1850 to 2014. We used a statistical method to derive fine resolution maps from GCM runs (CESM2, 10 runs). In the method, we searched for analogs in a database we constructed from the association between re-analyses large-scale meteorological fields (ERA5 and ERA-Interim) and RCM daily accumulated snowfall (RACMO2.3p5.5). RACMO2.3p5.5 data are available freely on request (<a href="https://www.projects.science.uu.nl/iceclimate/models/antarctica.php">https://www.projects.science.uu.nl/iceclimate/models/antarctica.php</a>). CESM2 CMIP6 runs are also freely available (<a href="https://esgf-node.llnl.gov/search/cmip6/">https://esgf-node.llnl.gov/search/cmip6/</a>). The complete description of the algorithm and performance is described in: <strong><em>Ghilain </em><em>N.,</em><em> Vannitsem </em><em>S.,</em><em> Dalaiden </em><em>Q.,</em><em> Goosse </em><em>H.,</em><em> De Cruz </em><em>L.,</em><em> </em><em>Wei</em><em> </em><em>W., </em><em>Reconstruction</em><em> of d</em><em>aily snowfall accumulation at 5.</em><em>5</em><em>km resolution over Dronning Maud Land, Antarctica, from 1850 to 2014 </em><em>using an analog-based downscaling technique</em></strong>, submitted to Earth System Science Data (ESSD).</p> <p>The MASS2ANT Snowfall dataset is composed of the annual estimations of snowfall over Dronning Maud Land, the daily time series for the total period for all the emerged grid points of the domain, the principal components time series and Empirical Orthogonal Functions (EOF) offering the possibility to analyze the synoptic weather patterns associated to snowfall over the ice sheet and the Principal Component weights (PCs) time series from the re-analysis in case one wants to extend or improve the database. Realistic weather patterns can be recomposed in associating (product of matrices) the PCs with the EOFs.</p> <p>Here (Daily fields - Part 3), we provide the daily snowfall time series resulting from the downscaling of the 3 last members of CESM2, using ERA-Interim (or ERA5) and RACMO2.3p5.5 for training.</p>
MASS2ANT Snowfall Dataset (Downscaling @5.5km over Dronning Maud Land, Antarctica, 1850 - 2014): Daily fields (Part 2)
<p>We provide in this dataset maps at 5.5 km resolution of the daily and yearly accumulated snowfall over emerged land (Ice Sheet) of Dronning Maud Land (Antarctica) from 1850 to 2014. We used a statistical method to derive fine resolution maps from GCM runs (CESM2, 10 runs). In the method, we searched for analogs in a database we constructed from the association between re-analyses large-scale meteorological fields (ERA5 and ERA-Interim) and RCM daily accumulated snowfall (RACMO2.3p5.5). RACMO2.3p5.5 data are available freely on request (<a href="https://www.projects.science.uu.nl/iceclimate/models/antarctica.php">https://www.projects.science.uu.nl/iceclimate/models/antarctica.php</a>). CESM2 CMIP6 runs are also freely available (<a href="https://esgf-node.llnl.gov/search/cmip6/">https://esgf-node.llnl.gov/search/cmip6/</a>). The complete description of the algorithm and performance is described in: <strong><em>Ghilain </em><em>N.,</em><em> Vannitsem </em><em>S.,</em><em> Dalaiden </em><em>Q.,</em><em> Goosse </em><em>H.,</em><em> De Cruz </em><em>L.,</em><em> </em><em>Wei</em><em> </em><em>W., </em><em>Reconstruction</em><em> of d</em><em>aily snowfall accumulation at 5.</em><em>5</em><em>km resolution over Dronning Maud Land, Antarctica, from 1850 to 2014 </em><em>using an analog-based downscaling technique</em></strong>, submitted to Earth System Science Data (ESSD).</p> <p>The MASS2ANT Snowfall dataset is composed of the annual estimations of snowfall over Dronning Maud Land, the daily time series for the total period for all the emerged grid points of the domain, the principal components time series and Empirical Orthogonal Functions (EOF) offering the possibility to analyze the synoptic weather patterns associated to snowfall over the ice sheet and the Principal Component weights (PCs) time series from the re-analysis in case one wants to extend or improve the database. Realistic weather patterns can be recomposed in associating (product of matrices) the PCs with the EOFs.</p> <p>Here (Daily fields - Part 2), we provide the daily snowfall time series resulting from the downscaling of the 7 first members of CESM2, using ERA-Interim and RACMO2.3p5.5 for training.</p>
Data and code for: Impacts of changing snowfall on seasonal complementarity of hydroelectric and solar power
<p>Data and code to reproduce analyses in manuscript entitled: Influence of changing snowfall on seasonal complementarity of hydroelectric and solar power. Submitted to Environmental Research: Infrastructure and Sustainability.</p> <p>The contents include the following scripts and files, listed below. Scripts are listed in the order needed to reproduce the analysis, though intermediate data products have been saved so it is not necessary to reproduce the initial analytical steps.</p> <ul> <li>R/ <ul> <li>eia923_860.R: extracts solar and hydropower production data; requires local download of EIA data.</li> <li>gridMET_swep.R: downloads and summarises gridmet data; does not require prior local download.</li> <li>fdr.R: function to calculate the p-value associated with a given false discovery rate as described in the associated manuscript.</li> <li>combine_data.R combines solar, hydropower, and SWE/P data</li> <li>analysis.Rmd: primary script in which analyses are conducted</li> </ul> </li> <li>data/ <ul> <li>annual_swep.csv: output from gridMET_swep.R with annual SWE/P for each watershed in the study</li> <li>monthly_hydro.csv: output from eia923_860.R</li> <li>monthly_solar.csv: output from eia923_860.R</li> <li>combined_variables.csv: combines variables above in one CSV</li> <li>watersheds_wbd_ss: shapefiles for watersheds that drain to each dam used in the study, derived as described in the manuscript.</li> </ul> </li> </ul>
Selected large model output files and Buffalo sounding data from: Lake Huron enhances snowfall downwind of Lake Erie: a modeling study of the 2010 near year’s Lake-effect snowfall event
Open the record for dataset details and reuse information.
Sea-effect snowfall in Finland
<p>This repository contains a list of detected sea-effect snowfall days and corresponding precipitation and lightning figures related to the ASR Special Issue: 19th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2019 journal article “Statistics of sea-effect snowfall along the Finnish coastline based on regional climate model data”, Adv. Sci. Res., 17, 87–104, 2020, <a href="https://doi.org/10.5194/asr-17-87-2020">https://doi.org/10.5194/asr-17-87-2020</a>.</p> <p>Repository includes:<br> 1. Dates_Sea-effect_Snowfall.xlsx<br> 2. TOTPREC_RCA4.zip<br> 3. SNOWFALL_RCA4.zip<br> 4. TOTPREC_FMIClimGrid_obs.zip<br> 5. RADAR_FMI.zip<br> 6. LightningStrokes.zip</p> <ul> <li>Dates_Sea-effect_Snowfall.xlsx includes a table of dates and areas where favorable conditions to produce sea-effect snowfall in Finland were fulfilled based on RCA4 data.</li> <li>TOTPREC_RCA4.zip includes figures of daily total precipitation during the detected sea-effect snowfall days as simulated by RCA4 (mm/day). Figure names include the date and area the sea-effect snowfall was detected.</li> <li>SNOWFALL_RCA4.zip includes figures of daily total snowfall during the detected sea-effect snowfall days as simulated by RCA4 (mm/day as water equivalent). Figure names include the date and area the sea-effect snowfall was detected.</li> <li>TOTPREC_FMIClimGrid_obs.zip includes figures of observed total precipitation during the detected sea-effect snowfall days by FMIClimGrid (mm/day).</li> <li>RADAR_FMI.zip: Radar reflectivity images (instantaneous) for at least one time during the sea-effect snowfall days (dBZ).</li> <li>LightningStrokes.zip: Lightning stroke images when lightning was observed (location of strokes and the UTC-time it was observed) during the detected sea-effect snowfall days.</li> </ul> <p> </p>
Large-Scale Atmospheric Drivers of Snowfall over Thwaites Glacier, Antarctica
<p>Monthly RACMO2 snowfall rates (1979-2015, in mm w.e. per month), as described in Lenaerts et al., 2018 (https://www.cambridge.org/core/journals/annals-of-glaciology/article/climate-and-surface-mass-balance-of-coastal-west-antarctica-resolved-by-regional-climate-modelling/E3DD6D0DA914C6031F96A548AF53603A), and used in this paper. </p>
Tibetan Plateau increases the snowfall in southern China: a refutation to some trending views
<p>Data for figures in "Tibetan Plateau increases the snowfall in southern China: a refutation to some trending views"</p>
Grazing regulates temperate grassland multidimensional stability facing extreme winter snowfall reductions by influencing below-ground bud density
Open the record for dataset details and reuse information.
Data from: Pivotal effect of early-winter temperatures and snowfall on population growth of alpine Parnassius smintheus butterflies
Geographic range shifts in species' distributions, due to climate change, imply altered dynamics at both their northern and southern range limits, or at upper and lower elevational limits. There is therefore a need to identify specific weather or climate variable(s), and life stages or cohorts on which they act, and how these affect population growth. Identifying such variables permits prediction of population increase or decline under a changing climate, and shifts in a species' geographic range. For relatively well studied groups, such as butterflies, geographic range shifts are well documented, but weather variables and mechanisms causing those shifts are not well known. The Holarctic butterfly genus Parnassius (Papilionidae) inhabits northern and alpine environments subject to variable and extreme weather. As such, Parnassius species are vulnerable not only to long-term changes in average conditions but especially to short-term extreme weather events. We use population growth estimates for the alpine butterfly, Parnassius smintheus, from 21 populations in the Rocky Mountains of Canada, over a 20-year interval, combined with techniques of machine learning (randomForests) and parametric modeling to identify the important weather variables determining population growth. We do this to determine the seasons and life-stages of P. smintheus most affected by climate change. Extreme minimum and maximum temperatures in November, in combination with November snowfall, affect annual population growth most, more so than do mean temperatures in November, and more so than weather at any other time of year. Populations decline both in years with low extreme minimum temperatures in November, and especially in years with high extreme maximum temperatures in November, indicating that overwintering eggs are particularly vulnerable to early-winter weather. Snowfall ameliorates the negative effects of extreme temperatures, particularly for extreme warm events. Results provide insight into biological mechanisms by which over-wintering eggs might be affected by early winter weather. Short-term extreme weather in November, acting on a single pivotal life-stage (egg) is a far better predictor of population change of alpine Parnassius smintheus butterflies than is the general index of climate, the Pacific Decadal Oscillation (PDO).
FIGURE 3 in Descriptions of two new and one newly recorded enchytraeid species (Clitellata, Enchytraeidae) from the Ozegahara Mire, a heavy snowfall highmoor in Central Japan
FIGURE 3. Globulidrilus helgei Christensen & Dózsa-Farkas, 2012 from Ozegahara-mire, Japan (NSMT-An 474–477), chaetae figures from fixed specimens, other figures from living specimens. A. Anterior body region (12 segments) of a mature specimen, from whole mount, body interior, dorsal view. B. Coelomocytes. C. Spermatheca. D. Ventral chaetae. E. Lateral chaetae.
FIGURE 2 in Descriptions of two new and one newly recorded enchytraeid species (Clitellata, Enchytraeidae) from the Ozegahara Mire, a heavy snowfall highmoor in Central Japan
FIGURE 2. Chamaedrilus ozensis sp. nov. from Ozegahara-mire, Japan (NSMT-An 468–473), chaetae figures from fixed specimens, other figures from living specimens. A. Anterior body region (7 segments) of a mature specimen, from whole mount, body interior, dorsal view, coelomocytes and vessel systems omitted. B. Clitellum dosally, with granulocytes (dotted) and hyalocytes (interspaces). C. Brain. D. Nephridia. E. Spermatheca. F. Sperm funnel. G. Coelomocytes. H. Ventral chaetae. I. Lateral chaetae.
FIGURE 1 in Descriptions of two new and one newly recorded enchytraeid species (Clitellata, Enchytraeidae) from the Ozegahara Mire, a heavy snowfall highmoor in Central Japan
FIGURE 1. Mesenchytraeus nivalis sp. nov. from Ozegahara-mire, Japan (NSMT-An 463), chaetae figures from fixed specimens, other figures from living specimens. A. Anterior body region (14 segments) of a mature specimen, body interior, dorsal view, coelomocytes omitted. B. Brain. C. Coelomocyte. D. Spermatheca (sperm omitted). E. Sperm funnel. F. Nephridium. G. Vas deferens with a fusiform atrium lying outside the penial bulb. H. Male pores and bursal slits. I. Sperm bundles. J. Ventral chaetae. K. Lateral chaetae.
ScienceDex guides
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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.