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21 results for “Cryosphere”
Near Pan-Svalbard cryospheric hazards inventory (SvalCryo)
<p>We present a comprehensive inventory of thaw slumps (TS) and thermo-erosion gullies (TEG) on the Svalbard Archipelago. We used the most recent orthophotos (0.5 x 0.5 m pixel size) acquired in 2009-2011 from the Web Map Services (WMS) of the Norwegian Polar Institute. TS and TEG were identified and digitised on-screen as polygons in the ETRS_1989_UTM_Zone_33N coordinate reference system. <span>TS and TEG were identified based on their morphology, digitised on-screen (maximum zoom was 1:1000) as polygons, and then individually quality checked in the GIS environment. This process was repeated twice, to avoid any bias in feature(s) mapping, first by a geomorphologist (first author) and then by an Arctic geologist (second author). The cryospheric inventory of the 14 regions (Andre<span>é</span> Land, Dickson Land, James I Land, Nordenski<span>ö</span>ld Land, Bünsow Land, Olav V Land, Sabine Land, Nathorst Land, Heer Land, Wedel Jarlsberg Land, Torell Land, S<span>ørkapp Land, </span>Barents<span>øya and Edgeøya) </span>totalises 8491 polygons, out of which 3679 are TS and 4812 are TEG. Within the attribute tables, there are eight columns comprising details about each polygon/feature, as follows: FID (ID showing the total number of polygons), Shape (Polygon), ID (each polygon from each region has associated an ID for both TS and TEG), Area (sq. m), Perimeter (m), MaxDistanc (calculated between two points along the polygon perimeter), Elongation (calculated as the maximum distance divided by the square root of the area), Region (the name of the region that the feature belongs to).</span></p>
Atmosphere-cryosphere interactions during the last phase of the LGM (21 ka BP) in the European Alps
<p>This dataset refers to: Del Gobbo, C., Colucci, R. R., Monegato, G., Žebre, M., and Giorgi, F.: Atmosphere-cryosphere interactions at 21 ka BP in the European Alps, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2022-43, in review, 2022. </p> <p> </p> <p>We used the regional climate model RegCM4 to investigate the physical processes sustaining the glacier extent during the Last Glacial Maximum (LGM) and pre-industrial time (PI) over the European Alps. After a bias-correction of precipitation and temperature data, we reconstructed the environmental equilibrium line altitude (envELA) of the Alpine glaciers, which resulted consistent with geological records. </p> <p>#----------------------------------------------------------</p> <p> </p> <p>LGM in the file names referes to 21 ka BP</p> <p>PI refers to pre-industrial</p> <p>#----------------------------------------------------------</p> <p> </p> <p><strong>This dataset contains:</strong></p> <p><strong>NetCDF files ------------------------------------------------------------------------------------</strong></p> <p> </p> <ul> <li><strong>Monthly mean TAS and PR</strong> <ul> <li>variables = <ul> <li>RegCM4 monthly mean near-surface air temperature (TAS)</li> <li>RegCM4 monthly mean precipitation (PR)</li> <li>model topography (topo)</li> </ul> </li> <li>units = TAS [°C], PR [mm/day], topo [m a.s.l.]</li> <li>model = RegCM4 (ICTP)</li> <li>method = RCM forced with MPI-ESM-P</li> <li>remapped = no</li> <li>resolution = 12 km</li> <li>files = <ul> <li>LGM_PR_TAS_monmean.nc</li> <li>PI_PR_TAS_monmean.nc</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Bias-corrected monthly mean TAS and PR</strong> <ul> <li>variables = <ul> <li>model topography (topo)</li> <li>Bias-corrected RegCM4 monthly mean precipitation (PR)</li> <li>Bias-corrected RegCM4 monthly mean near-surface air temperature (TAS)</li> </ul> </li> <li>units = TAS [°C], PR [mm/day], topo [m a.s.l.]</li> <li>model = RegCM4 (ICTP)</li> <li>method = bias-correction based on HISTALP (TAS) and LAPrec (PR) of RegCM4 data</li> <li>remapped = onto HISTALP grid</li> <li>resolution = 5 arcmin</li> <li>files= <ul> <li>LGM_PR_TAS_monmean_BC.nc</li> <li>PI_PR_TAS_monmean_BC.nc</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>ELA</strong> <ul> <li>variables = <ul> <li>ELA </li> <li>average RegCM-HISTALP-LAPrec topography</li> </ul> </li> <li>units = m a.s.l.</li> <li>data = calculated from bias-corrected RegCM4 data</li> <li>method = Zebre et al. (2020)</li> <li>remapped = on HISTALP grid</li> <li>resolution = 5 arcmin</li> <li>files = <ul> <li>LGM_ELA.nc</li> <li>PI_ELA.nc</li> </ul> </li> </ul> </li> </ul> <p><br> <strong>csv files ------------------------------------------------------------------------------------</strong></p> <p><strong>* dates refer to model dates, not real ones!!!</strong><br> tj_700_hpa_pr_lgm : Tagliamento glacier daily wind and precipitation at the 21 ka BP<br> tj_700_hpa_pr_pi : Tagliamento glacier daily wind and precipitation at the PI<br> db_700_hpa_pr_lgm : Dora Baltea glacier daily wind and precipitation at 21 ka BP<br> db_700_hpa_pr_pi : Dora Baltea glacier daily wind and precipitation at the PI<br> r_700_hpa_pr_lgm : Rhine glacier daily wind and precipitation at 21 ka BP<br> r_700_hpa_pr_pi : Rhine glacier daily wind and precipitation at the PI<br> ist_700_hpa_pr_lgm : Inn-Salzach-Traun glacier daily wind and precipitation at 21 ka BP<br> ist_700_hpa_pr_pi : Inn-Salzach-Traun glacier daily wind and precipitation at the PI</p> <p> </p>
CPAZMAL: Cryosphere PAZ satellite MAchine Learning
<p>CPAZMAL:<strong> C</strong>ryosphere <strong>PAZ</strong> satellite <strong>MA</strong>chine <strong>L</strong>earning</p> <p>The aim of this dataset is to serve as a foundation for machine learning in multi-class classification, specifically in mountainous regions. It comprises descending images acquired by the PAZ X-band satellite, focusing on the Mont Blanc region during the period from January 2020 to November 2021, totaling 56 acquisitions.</p> <p>The time series is divided into two sub-sections:</p> <ol> <li>From January 2020 to 8th January 2021 included: dual polarisation HH and HV,</li> <li>After 8th January 2021: single polarisation HH.</li> </ol> <div> <div>The datas are divided into 8 classes:</div> <div> <ul> <li>Hanging Glacier (HAG)</li> <li>Ice Aperon (ICA)</li> <li>Ablation area</li> <li>Accumulation area</li> <li>Rock</li> <li>Plain</li> <li>Forest</li> <li>City</li> </ul> <p>In each classe, between 4 to 10 groups or distinct areas, where their complete description (position, aspect, elevation, ...) can be found in the <em>desc_topo_areas.png </em>file</p> </div> <div>We provide code that directly extracts temporal or spatial datasets, consisting of homogeneous windows paired with respective labels.</div> <div> <pre><code># Request and save data into hdf5 file rqtemp = "classe in ['ICA','HAG','ABL','ACC','FOR','CIT','ROC','PLA'] & date < '2021-01-01'" cdlf = Dataset_tiff2hdf5 ( path_to_folder_extracted, different_group=True, n_jobs=1, outpath="path_to_dataset.h5", extension="temporal" ) cdlf.extract_data(rqtemp, polarisation="HH", winsize=7, save=True) # Load the previously extracted data set ( x, y, gr, org, _, ) = load_h5(path_to_dataset.h5)</code></pre> </div> <div>An example of how to use it can be found at <a href="https://github.com/Matthieu-Gallet/PAZ_DTW_classification" target="_blank" rel="noopener">Github</a>.</div> <div> </div> <blockquote> <div>The authors would like to thank the <em>Spanish Instituto Nacional de Tecnica Aerospacial</em> (INTA) for the PAZ images (Project AO-001-051) </div> </blockquote> </div>
SUMMA/mizuRoute model configurations, parameters, and ensemble statistics for representative cryosphere basins
<p>Meteorological forcing is a major source of uncertainty in hydrological modeling. The recent development of probabilistic large-domain meteorological datasets enables convenient uncertainty characterization, which however is rarely explored in large-domain research. Tang et al. (2023) analyze how uncertainties in meteorological forcing data affect hydrological modeling in 289 representative cryosphere basins by forcing the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models with precipitation and air temperature ensembles from the Ensemble Meteorological Dataset for Planet Earth (EM-Earth). EM-Earth probabilistic estimates are used in ensemble simulation for uncertainty analysis. The results reveal the magnitude, spatial distribution, and scale effect of uncertainties in meteorological, snow, runoff, soil water, and energy variables.</p>
Kittel et al. (2021), The Cryosphere : MAR and ESMs data
<p>Outputs used in:</p> <p><em>Kittel, C., Amory, C., Agosta, C., Jourdain, N. C., Hofer, S., Delhasse, A., Doutreloup, S., Huot, P.-V., Lang, C., Fichefet, T., and Fettweis, X.: Diverging future surface mass balance between the Antarctic ice shelves and grounded ice sheet, The Cryosphere https://doi.org/10.5194/tc-2020-291,2021.</em></p> <ul> <li>MAR outputs with yearly values of SMB and components over the Antarctic ice sheet (1980--2100)</li> <li>Grid file used in MAR simulations</li> <li>ESM and GCM yearly near-surface temperature over 1960--2100 (as downloaded from the ESGF nodes)</li> </ul> <p><br> Associated daily datasets:<br> MAR(ACCESS1.3): 10.5281/zenodo.4525735<br> MAR(NorESM1-M): 10.5281/zenodo.4528998<br> MAR(CESM2): 10.5281/zenodo.4529002<br> MAR(CNRM-CM6-1): 10.5281/zenodo.4529004<br> <br> <br> If you need other variables or output frequencies from MAR, write me (c2kittel@gmail.com) and I will be glad to help you. I will also be happy to share the scripts I have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR outputs.<br> <br> Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below that contained both informations related to MAR and CMIP5/6. In order to document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact C. Kittel to add their works in the list of MAR-related publications. </p> <p>"We thank C. Kittel and the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. "</p> <p>You should also refer to and cite the following paper:</p> <p>Kittel, C., Amory, C., Agosta, C., Jourdain, N. C., Hofer, S., Delhasse, A., Doutreloup, S., Huot, P.-V., Lang, C., Fichefet, T., and Fettweis, X.: Diverging future surface mass balance between the Antarctic ice shelves and grounded ice sheet, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2020-291, accepted, 2020.</p>
Dataset used for "Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia"
<p><strong>General Info</strong></p> <p>This dataset contains monthly output from two 20-year (1979-1998) variable-resolution (VR) CESM2 simulations (HMA_VR7a and HMA_VR7b). The coupled atmosphere-land simulations were run with a newly generated VR grid that has regional grid refinements up to 7 km over High Mountain Asia. The HMA_VR7b simulation was performed with an updated glacier-cover dataset (<a href="https://doi.org/10.5281/zenodo.7864689">https://doi.org/10.5281/zenodo.7864689</a>) and includes snow and glacier model modifications. Further, monthly output from a globally uniform 1-degree CESM simulation (NE30), used for evaluation of the HMA VR simulations, is also included. The monthly output have been used for analysis and discussion in the paper “<em>Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia” </em>that is currently under review in the Cryosphere Discussions, <a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>.</p> <p><strong>Contact</strong></p> <p>René Wijngaard (<a href="mailto:r.r.wijngaard.uu@gmail.com">r.r.wijngaard.uu@gmail.com</a> / <a href="mailto:r.r.wijngaard@uu.nl">r.r.wijngaard@uu.nl</a>) </p> <p><strong>Raw Data</strong></p> <p>Raw monthly and daily unstructured HMA VR model output are available on request. </p> <p><strong>Dataset Contents</strong></p> <pre><code>NE30.tar HMA_VR7a.tar HMA_VR7b.tar </code></pre> <p>These files contain atmosphere (CAM) and land (CLM) model output that are regridded to a 1-degree finite volume (0.9 x 1.25 degrees latitude/longitude) grid. The following variables are included: CLDLIQ, OMEGA, Q, STEND_CLUBB, SWCF, T, Z3, EFLX_LH_TOT, FGR, FIRE, FLDS, FSA, FSDS, FSH, FSM, FSNO, FSM, FSR, H2OSNO, PCT_LANDUNIT, QICE_MELT, QSNOFRZ, RAIN, SNOW, and TSA. </p> <pre><code>SMB_HMA_VR7a.tar SMB_HMA_VR7b.tar</code></pre> <p>These files contain unstructured SMB-related CLM model output (i.e., on the HMA VR grid). The following variables are included: PCT_LANDUNIT, QRUNOFF_ICE, QSNOFRZ_ICE, QSNOMELT_ICE, QSOIL_ICE, RAIN_ICE, and SNOW_ICE.</p>
Cryosphere Inland Water Greenhouse Gases Database (CIWD-GHG)
<p>This is a database called Cryosphere Inland Water Greenhouse Gases Database (CIWD-GHG), which provides spatial-temporally resolved greenhouse gases (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) data from cryosphere inland waters (lakes, ponds, reservoirs, rivers, and streams). The dataset was created through a synthesis procedure.</p> <p>To compile the dataset, we conducted searches in various sources including peer-reviewed papers, dissertations, theses, and public data repositories. The searches were conducted using platforms such as Web of Science, Google Scholar, ProQuest Dissertations & Theses Global, China National Knowledge Infrastructure, Arctic Data Center, Zenodo, Environmental Data Initiative, and PANGAEA. The search string is: (methane OR CH4 OR carbon dioxide OR CO2 OR nitrous oxide OR N2O OR greenhouse*) AND (river OR stream OR lake OR pond OR reservoir) AND (Arctic* OR Tibet* OR Greenland OR Antarctic OR glacier* OR permafrost). We ensured completeness by conducting multiple searches before April 2023.</p> <p>We applied a consistent criterion for screening and selecting the searched results. Specifically, we included data on greenhouse gas concentrations or fluxes measured in inland water systems such as streams, rivers, ponds, lakes, and reservoirs that are associated with permafrost or glaciers. The study focused on the cryosphere extent, excluding sites outside this extent. Wetland ecosystems and inland waters in cold regions without glaciers or permafrost were also excluded. Additionally, floodplain lakes connected with river channels were excluded, except for those barely connected with high-closure river channels, which were included as lake sites. Gas concentration data in permanently ice-covered water bodies were not included. We also collected auxiliary data on waterbody physical and chemical characteristics, climate, and land cover to the extent possible.</p> <p>The dataset was divided into two separate files: CryoLake.xlsx and CryoRiver.xlsx. CryoLake.xlsx contained data on lakes, ponds, and reservoirs, while CryoRiver.xlsx contained data on rivers and streams. Each file consisted of four sub-tables: source table (data sources), sites table (sites information), concentration table (concentration data), and flux table (flux data). All sub-tables were linked using unique source IDs, and the sites, concentration, and flux tables were further linked using unique site IDs. The temporal resolution varied, with daily data being the shortest resolution. Sub-daily measurements were averaged to daily data, and data reported in monthly, seasonal, and annual scales were also included. Considering the difficulty in obtaining cryosphere-related GHG data, we included all available data. The spatial resolution primarily focused on the plot scale, although aggregated sites were also included. Detailed spatiotemporal information of the measured data was recorded in the data table for further analysis. Due to regional heterogeneity, we did not have a uniform standard for dividing seasons. Instead, seasons were assigned based on the site descriptions provided in each study.</p> <p>The R script file is the multilevel bootstrap method used for Inland water greenhouse gas emissions upscaling. The ziped file contains bootstrap results for further calculating zonal and monthly GHG emissions.</p>
Aerosol Optical Depth (AOD) over the Nepalese cryosphere derived from an empirical model
<p>In this manuscript, we demonstrate the spatial and temporal variability of AOD over the Nepalese cryosphere may be reconstructed through empirical relationships developed from numerical weather model analysis fields and geographic variables. Our manuscript is novel, providing:<br> * a capability to extrapolate AOD over the cryospheric portion of Nepalese Himalayas<br> * an understanding of the relationships between ECMWF Reanalysis with observed AOD<br> * and a cross-validation of an empirical model using MODIS derived AOD and AERONET data.</p> <p>The empirical model that we present in this study, could potentially be applied to other mountains regions. We believe that the findings presented in our manuscript provide a novel approach to address the challenge of paucity of observations for aerosols in the cryospheric portion in Himalaya region. </p> <p> </p> <p> </p>
Datasets as used in Beckmann et al. The Cryosphere
<p>These are the data sets for all 12 glacier. The catchment area of each glacier can be found in _catchment_areas.</p> <p>The derived 1D glacier geometry of each glacier is found in glacier_name/glacier.nc. It contains glacier values as well as the lon,lat coordinates of the center line. In glacier_name/out/restart.nc is the stable state version after running the model with correction flux (command found in glacier_name/out/command). Restart.nc was then run for another 100 years with the same parameters to assure a quasi-stable state. Then the plume model was switched on an parameters fwd and beta where altered as described in Beckmann et al.(2019).</p> <p>As depicted in the Supporting Information of Beckmann et al. (2019) , the folders also contain the temperature -salinity-profiles of each glacier fjord from CTD measurements(Oceanfjord*.nc) and reanalysis data (*_ocean.nc), as well as the subglacial discharge (subglacial_discharge.nc) and surface mass balance development (MAR_MIROC5_ref_NCEP_rcp85_1900_2100_100m.nc) under RCP 8.5.</p> <p> </p>
Distribution maps of Biosphere and Cryosphere tipping elements
<p>This dataset contains a collection of sources that mark the geographical areas of regions/elements/biomes of the Biosphere and the Cryosphere identified as tipping elements on various temporal and spatial scales by the Global Tipping Points Report 2023 (https://global-tipping-points.org) based on:<br>- Armstrong McKay, Sakschewski, Roman-Cuesta et al., In prep.<br>- Winkelmann, Steinert, Armstrong McKay et al., In prep.</p><p>Below you find the data sources for the collection of this dataset. Note that the biosphere data sources provide more biomes than listed here, as only those identified as tipping element are selected here. For the full datasets, including non-tipping biomes, please refer to the original sources listed below.<br> </p><p>BIOSPHERE:</p><p>1. Ecoregions 2017 biomes: https://ecoregions.appspot.com</p><p>Note that only the following biomes were selected: "Tundra", "Tropical & Subtropical Moist Broadleaf Forests","Tropical & Subtropical Coniferous Forests", "Tropical & Subtropical Dry Broadleaf Forests", "Boreal Forests/Taiga", "Temperate Broadleaf & Mixed Forests","Temperate Conifer Forests", "Temperate Grasslands, Savannas & Shrublands","Tropical & Subtropical Grasslands, Savannas & Shrublands","Flooded Grasslands & Savannas","Montane Grasslands & Shrublands", "Deserts & Xeric Shrublands","Mediterranean Forests, Woodlands & Scrub", "Mangroves".</p><p>2. Keith, David A., Ferrer-Paris, Jose R., Nicholson, Emily, ..., Kingsford, Richard T. (2020). Indicative distribution maps for Ecological Functional Groups - Level 3 of IUCN Global Ecosystem Typology [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3546513; <br>and Keith et al. 2022: https://www.nature.com/articles/s41586-022-05318-4</p><p>Note that only the following biomes were selected: "F2_1_Large_perm_lakes", "F2_2_Small_perm_lakes", "F2_4_Freeze-thaw_lakes", "F2_6_Perm_salt_lakes", "M1_1_Seagrass_meadows", "M1_2_Kelp_forests", "M1_3_Photic_coral_reefs", "M4_2_Marine_aquafarms", "M1_5_Marine_animal_forests".<br> </p><p>CRYOSPHERE:</p><p>1. Land permafrost: Obu, Jaroslav; Westermann, Sebastian; Kääb, Andreas; Bartsch, Annett (2018): Ground Temperature Map, 2000-2016, Northern Hemisphere Permafrost. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, https://doi.org/10.1594/PANGAEA.888600</p><p>2. Subsea permafrost: Overduin, Pier Paul; Schneider von Deimling, Thomas; Miesner, Frederieke; Grigoriev, Mikhail N; Ruppel, Carolyn D; Vasiliev, Alexander; Lantuit, Hugues; Juhls, Bennet; Westermann, Sebastian; Laboor, Sebastian (2020): Submarine Permafrost Map (SuPerMAP), modeled with CryoGrid 2, Circum-Arctic. PANGAEA, https://doi.org/10.1594/PANGAEA.910540</p><p>3. Sea Ice: Keith, David A., Ferrer-Paris, Jose R., Nicholson, Emily, ..., Kingsford, Richard T. (2020). Indicative distribution maps for Ecological Functional Groups - Level 3 of IUCN Global Ecosystem Typology [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3546513; <br>and Keith et al. 2022: https://www.nature.com/articles/s41586-022-05318-4</p><p>4. Antarctica: Medium resolution vector polygons of the Antarctic coastline (2014) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/ed0a7b70-5adc-4c1e-8d8a-0bb5ee659d18</p><p>5. Greenland: Morlighem M. et al., (2017), BedMachine v3: Complete bed topography and ocean bathymetry mapping of Greenland from multi-beam echo sounding combined with mass conservation, Geophys. Res. Lett., 44, doi:10.1002/2017GL074954</p><p>6. Glaciers: RGI Consortium, . (2012). Randolph Glacier Inventory - A Dataset of Global Glacier Outlines, Version 2 [Data Set]. Boulder, Colorado USA. National Snow and Ice Data Center. https://doi.org/10.7265/cc6e-zp12.<br> </p><p>For any questions regarding the dataset, please free feel to contact Norman J. Steinert (nste@norceresearch.no, normanst@ucm.es)</p>
Data and GrADS scripts for "Changes in March mean snow water equivalent since the mid-twentieth century and the contributing factors in reanalyses and CMIP6 climate models", submitted to The Cryosphere
<p>Data and GrADS (Grid Analysis and Display System) scripts for reproducing the figures and numerical results included in the manuscript "Changes in March mean snow water equivalent since the mid-twentieth century and the contributing factors in reanalyses and CMIP6 climate models". Revised for The Cryosphere in March 2023.</p> <p>In addition to the README file, there are two zipped archives:</p> <p>swe_trends.zip (2.3 GB) includes both the data (mostly as GrADS binaries), the GrADS data descriptor files and the scripts.</p> <p>swe_trends_no_data.zip (74 kB) includes just the scripts and the data descriptor files.</p> <p>Please see the README file for further details on the content and use of the archives.</p>
Danesi_et_al_Cryosphere_2023-RAW_EVENTS_Waveform
<p>The repository contains 2 zipped files with waveforms related to the paper Danesi et al. 2023, Cryosphere.</p> <p>The files are:</p> <p>- cycled_events_2003-04.tar.gz : containing 3-component waveforms for a single station used in the match-filtering analysis</p> <p>- events_waveforms.tar.gz : containing 3-component waveforms for a single station used for absolute location analysis</p>
Rapid Radiative Transfer Model Output Evaluating Cryospheric Surface Emissivities
<p>Rapid Radiative Transfer Model (RRTM) (Mlawer, et al.,1997) output use to evaluate three cryospheric surface emissivities spectrally across three atmospheric profiles. README file attached provides information regarding output format. </p>
Data from: Cryospheric hydrometeorology observation in the Hulu Catchment (CHOICE), Qilian Mountains, China
Understanding cryospheric hydrology and the effects of cryospheric changes on river runoff is critical for sustainable water management, especially in arid inland river basins, such as those in Northwest China, where water resources mainly come from alpine areas. A cryospheric hydrometeorology observation system (CHOICE) has been established since 2008 in the Hulu Catchment, which is a well instrumented experimental and representative catchment in the upper reaches of the inland Hei River, Qilian Mountains, Northwest China. The CHOICE includes dense meteorological measurements from 2,980 to 4,800 m a.s.l., such as glacier, snow and permafrost hydrology; water and heat balance in the vertical landscape zones, including alpine grassland, meadow, shrub, coniferous forest, marshy meadow and moraine-talus zones. The comprehensive of long-term observations available for the CHOICE provides the basis for model development and application in cryospheric hydrological research. We try to study on cryospheric hydrometeorological process of precipitation, freeze-thaw cycle, energy balance, soil-vegetation-atmosphere-transfer (SVAT), runoff, groundwater reservoir and hydrological resiliency within vertical altitude in CHIOCE. In addition, the CHOICE of data sharing are mainly through website (http://hhsy.casnw.net/) and WestDC database (http://westdc.westgis.ac.cn/). The CHOICE, as implied by as its name, is an open cryospheric hydrology observation and research system.
ICESat, ERS1, ERS2, Envisat Laser and Radar Altimetry Datasets for the Cryosphere model Comparison Tool (CmCt) Input for Greenland and Antarctica
<div> <p>These datasets contain the ICESat, ERS1, ERS2, Envisat Laser and Radar Altimetry Datasets for CmCt Input data for Greenland and Antarctica. These reference observational datasets are used in the CmCt to compare ice sheet models with.</p> <p>The <strong>ICESat/GLAS</strong> instrument was a lidar altimeter and the primary instrument on the NASA ICESat mission. It took point elevation measurements approximately every 170 meters along its track, and each shot had a footprint of approximately 70 meters in diameter.</p> <p>The GLAS instrument contained 3 lasers, but due to some instrumentation issues, it was decided to turn the lasers on and off during predetermined time periods. For more detailed information about GLAS and the ICESat mission, visit the <a href="http://icesat.gsfc.nasa.gov/icesat/" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">ICESat website</a>.</p> <p>For use with the CmCt project, the Greenland elevation data from ICESat/GLAS (Zwally et al, 2002) were preprocessed. The data were cleaned and limited to the ice sheets. At the time of creation the 634 release of the <a href="https://nsidc.org/data/GLA12/versions/34" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">GLAS12</a> product was used (<em>Zwally et al, 2014</em>). </p> <p>The processing was accomplished by:</p> <ol> <li>restricting the data to GLAS data points only on the ice surface</li> <li>applying two data quality filters we required the GLAS surface reflectivity to be > 0.0375 and we required the uncertainty associated with the GLAS fitting procedure to be < 0.0375 (the numerical coincidence is in fact a coincidence). These are the same quality criteria that were used for <a href="http://imbie.org/" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">IMBIE2</a> and thus are being implemented for the CmCt.</li> <li>checking the data against the reerence DEM (GIMP 90-m DEM for Greenland or Bamber 1-km DEM for Antarctica), requiring the elevation difference to be < 200m.</li> </ol> <p>Please find more details on the data preprocessing in the Supporting Docs tab.</p> <p>The<a href="https://www.esa.int/Applications/Observing_the_Earth/Envisat" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer"> <strong>Envisat</strong></a> (Environmental Satellite), <strong><a href="https://eoportal.org/web/eoportal/satellite-missions/e/ers-1" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">ERS1</a></strong>, <strong><a href="https://eoportal.org/web/eoportal/satellite-missions/e/ers-2" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">ERS2</a></strong> (European Remote Sensing Satellites 1 and 2) radar altimeter datasets were also preprocessed to prepare the data to generate a comparison data set for the CmCt. Several filters were used to remove data that are not on the ice sheet or have questionable elevations. Please see the detailed processing descriptions in the Supporting Docs.</p> <p>Radar and laser altimeters measure similar parameters. They measure the time of flight of photons from the spacecraft to the reflection point and back to the spacecraft. The time of flight is then used to calculate an elevation. Accurate elevations require precise knowledge of the spacecraft orbit, corrections for atmospheric scattering, and other factors.</p> <p>There are several differences between the radar and laser altimetry data available here that should be noted:</p> <ul> <li>The accuracy of the elevations calculated from the radar data is generally lower than the accuracy of elevations based on laser data, primarily because <ul> <li>the radar beam is much broader (several km by the time it reaches the ground vs < 100 m for the laser beam).</li> <li>the radar photons penetrate snow and ice a significant amount (cm to m), whereas the laser photons from ICESat penetrate minimally if at all.</li> </ul> </li> <li>ERS and Envisat worked at a lower pulse rate than ICESat, and had a shorter repeat period, so the data are sparser on the ground (but repeat approximately monthly). On the other hand, the radar satellites worked continuously, whereas ICESat only operated for 2-3 months per year. </li> <li>The radar data collectively cover a longer period of time, starting more than a decade earlier and extending past the end of the ICESat data.</li> <li>ERS and Envisat were in orbits that left larger holes at the poles than ICESat (8.5 degrees for the radar satellites vs 4 degrees for ICESat).</li> <li>Radar beams penetrate clouds, whereas the ICESat laser beam was scattered by clouds, with returns becoming unusable if the optical depth was much greater than 1.</li> </ul> <h4> </h4> <h4>Laser and Radar Altimetry Available Data Time Range:</h4> <h4> </h4> <table> <tbody> <tr> <td>ERS1:</td> <td>1991-1995</td> </tr> <tr> <td>ERS2:</td> <td>1996-2002</td> </tr> <tr> <td>Envisat:</td> <td>2003-2012</td> </tr> <tr> <td>ICESat/GLAS:</td> <td>2003-2009</td> </tr> </tbody> </table> <h4> </h4> <h4>Downloading Data</h4> <p><a href="https://theghub.org/resources?id=4737"><strong>The data can be downloaded from the Globus GHub-CmCt endpoint. Please log in and click on the Download tab to receive the Download instructions.</strong></a></p> </div> <h4>References</h4> <div> <p>Howat, I. M., A. Negrete, and B. E. Smith, The Greenland Ice Mapping Project (GIMP) land classification and surface elevation data sets, The Cryosphere 8.4 (2014): 1509-1518.</p> <p> </p> <p>Howat, I. M., A. Negrete, and B. E. Smith. MEaSUREs Greenland Ice Sheet Mapping Project (GIMP) Digital Elevation Model, Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center (2015).</p> <p> </p> <p>Zwally, H. J., et al. ICESat's laser measurements of polar ice, atmosphere, ocean, and land, Journal of Geodynamics 34.3 (2002):405-445.</p> <p> </p> <p>Zwally, H. J., et al. GLAS/ICESat L2 Antarctic and Greenland ice sheet altimetry data V034, National Snow and Ice Data Center, Boulder, Colorado (2014).</p> </div>
Data from: Cryospheric hydrometeorology observation in the Hulu Catchment (CHOICE), Qilian Mountains, China
Open the record for dataset details and reuse information.
Data and results for manuscript "A monitoring system for spatiotemporal electrical self-potential measurements in cryospheric environments"
<p>This package contains data and python scripts required to reproduce the figures of the accepted manuscript</p> <p>Weigand, M., Wagner, F. M., Limbrock, J. K., Hilbich, C., Hauck, C., and Kemna, A.: A monitoring system for spatiotemporal electrical self-potential measurements in cryospheric environments, Geosci. Instrum. Method. Data Syst. Discuss., https://doi.org/10.5194/gi-2020-5, 2020.</p> <p>Final Paper DOI: https://doi.org/10.5194/gi-9-1-2020</p> <p>https://gi.copernicus.org/preprints/gi-2020-5/</p> <p>Please refer to the Readme.txt file for further instructions.</p>
On thin ice: Solar geoengineering to manage tipping element risks in the cryosphere by 2040
<div>Table S1 shows the three-year schedule needed to certify a special tanker modification. Table S2 and Table S3 provide sources for data that the authors gathered on the cost, annual movements, total number of runways, and construction timelines for the 17 airports included in the paper.</div>
MEaSUREs Northern Hemisphere State of Cryosphere Weekly 100km EASE-Grid 2.0 V001
This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, reports the location of Northern Hemisphere snow cover and sea ice extent, the status of melt onset across Greenland and Arctic sea ice, and the level of agreement between snow cover maps derived from two different sources.
MEaSUREs Northern Hemisphere State of Cryosphere Daily 25km EASE-Grid 2.0 V001
This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, reports the location of Northern Hemisphere snow cover and sea ice extent, the status of melt onset across Greenland and Arctic sea ice, and the level of agreement between three different snow cover data sources.
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