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1,337 results for “antarctica”
Environmental, molecular, and life history data associated with ecological and evolutionary nematode responses to soil phosphorus availability, McMurdo Dry Valleys, Antarctica
Elemental stoichiometry is a useful theoretical framework for understanding the sources and controls on nutrient availability that can structure the composition, diversity, and life history of biotic communities. One such relationship, as postulated by the growth rate hypothesis (GRH), is that organismal development rate is positively linked to cellular phosphorus (P). To test the GRH as part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) program, we examined the effects of phosphorus (P) availability both in situ and in vitro, on the evolution of growth and development of free-living soil nematodes (primarily Plectus murrayi) that occur in the McMurdo Dry Valleys of Antarctica. During the 2008-2009 austral summer, we collected soils from two glacial till sequences, the Ross Sea till and Taylor II till, occurring in the Lake Fryxell and Lake Bonney basins, respectively, of Taylor Valley. Through a variety of subsequent analyses, we generated the environmental, molecular, and life history trait data contained herein. In addition, this package contains body size and biomass data for nematodes isolated from soil samples collected during the 1999-2000 and 2004-2005 austral summers.
McMurdo Dry Valleys LTER: Transplant Lake Integrated Connectivity Experiment (tLICE) in Taylor Valley, Antarctica, January 2018
The purpose of this experiment, performed as part of the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) program, was to investigate the impact of lake level rise and moat expansion on microbial community diversity and function in the East Lobe of Lake Bonney, located in Taylor Valley, Antarctica. The “tLICE†experiment tested the following MCM5 Hypotheses: H3-Disturbance increases connectivity and accelerates shifts towards homogeneity, and H4-Decreased heterogeneity reduces community resistance and resilience. Lake water from the East Lobe of Lake Bonney was collected at depths representing maximum phytoplankton productivity (5 m, 17 m, 23 m), and transferred to dialysis bags. The bags were then attached to PVC frames and transplanted to new locations within the lake in two experiments: 1) a moat transplant, in which 5 m communities were transplanted to the open water moat; and 2) shallow to deep transplant, in which communities from all three sampling depths were moved 3 m down the water column to mimic approximately one decade of lake level rise. Transplanted samples were incubated under these conditions for two weeks, and then frames were recovered and samples were processed for a number of measurements. This data package includes the following measurements: nutrient concentration, chlorophyll-a content, chlorophyll fluorescence, cell density, amplicon gene sequences (16S and 18S rRNA genes), and physical parameters.
Weekly CTD profile measurements of conductivity, specific conductance, temperature, depth, density, and salinity from Lake Hoare, McMurdo Dry Valleys, Antarctica during the 2012-2013 austral summer
As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, we investigated relationships between wind conditions and barotropic seiches within Lake Hoare, located in Taylor Valley, Antarctica, during the 2012-2013 austral summer. Temporal changes in the water column were measured using a rugged, handheld, CastAway CTD (conductivity-temperature-depth probe, manufactured by SonTek), deployed through the Lake Hoare Limno Hole (note, this is a separate instrument from the SeaBird CTD used as part of the MCM LTER core limnological monitoring program). The CastAway CTD directly measures temperature, electrical conductivity, and pressure at 5 Hz as it free-falls through the water column at a rate of approximately 1 m s-1 on the downward cast and approximately 0.3 m s-1 on the upward cast. The device calculates salinity and density using the International Equation of State for Seawater, called EOS-80. This data package provides 27 profiles of depth, temperature, conductivity, specific conductance, salinity, and density collected between November 25, 2012 and January 21, 2013. In general, three profiles were collected over a ten-minute-period every seven days for 57 days. Five profiles were collected prior to the arrival of spring melt, four profiles were collected during the arrival of spring melt on December 7, 2012, and 18 profiles were collected after the arrival of spring melt continuing into late summer.
McMurdo Dry Valleys LTER: Stoichiometry Experiment in Taylor Valley, Antarctica from 2007 to 2016
Soil communities in the McMurdo Dry Valleys are subject to many limitations, including resource limitations. However, the nutrients that are predominantly limiting to growth and diversity of soil biota are not known. Additionally, landscape history (N deposition, P weathering) and native N and P content (glacial till provenance) may influence the ability of soil communities to respond to nutrient additions or changes in nutrient availability associated with environmental change. Long-term experiments in the Bonney and Fryxell basins have been established with yearly application of aqueous nutrient additions: C, N, P, CN, and CP. Multiple nutrients additions were made at the Redfield Ratio of 106:16:1 (C:N:P). Responses of soil chemistry, CO2 flux, and biota are reported.
Ammonium desorption of hyporheic sediments from Von Guerard Stream, McMurdo Dry Valleys, Antarctica (January 2019)
This data package provides analytic results for a laboratory assay on sediment samples collected in January 2019 from a transect (“P2”) across Von Guerard Stream (as described by Heindel et al. 2021), located in Taylor Valley, McMurdo Dry Valleys, Antarctica. We used excess sample from that study in a laboratory desorption assay to assess conductivity dependent desorption of ammonium. The assay was performed by mixing samples with potassium chloride treatment solutions that replicate the range of specific conductivities observed in Von Guerard Stream, shaking and centrifuging slurries, and analyzing filtered supernatant.
Pond diatom communities from the McMurdo Sound region of Antarctica, assessed from modern samples (2012-2016) and from historical samples collected during Shackleton’s Nimrod Expedition (1908-1909)
We performed a field survey to characterize diatom communities living in the benthic microbial mats of ponds across the McMurdo Sound region of Antarctica. Samples were collected during the Austral summers of 2012-13, 2013-14, and 2015-16 from the McMurdo Dry Valleys, Cape Royds, and from Hut Ridge near McMurdo Station. We also characterized pond diatom communities in samples collected on various dates from 1908-1909 during Ernest Shackleton’s Nimrod Expedition to compare against modern samples. Historical samples were taken from Cape Royds and the Stranded Moraines, and were analyzed from slides stored at the Natural History Museum, London. This data package includes relative abundance data for diatom species found in these modern and historical samples.
The GRISLI-LSCE contribution to ISMIP6-Antarctica
<p>The GRISLI-LSCE contribution to ISMIP6-Antarctuca. Experiments IDs as in Nowicki et al.:</p> <p>Nowicki, S., Payne, A. J., Goelzer, H., Seroussi, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A., Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J., Hatterman, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C. M., Morlinghem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R.: Experimental protocol for sealevel projections from ISMIP6 standalone ice sheet models, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-322, in review, 2020.</p>
Spatial variability of the snowmelt-albedo feedback in Antarctica
<p>This dataset accompanies the manuscript "Spatial variability of the snowmelt-albedo feedback in Antarctica" by C.L. Jakobs et al., submitted to JGR: Earth Surface.</p> <p>File naming convention:</p> <p>Modelversion_domain_parameter_timeresolution_beginyear_endyear_experiment.nc</p> <p>where "experiment" is optional.</p> <p> </p> <p>Parameter names (note that precipitation and snowmelt are provided in units <em>per second</em>):</p> <ul> <li>LWin: downward longwave radiation [W/m<sup>2</sup>]</li> <li>LWout: upward longwave radiation [W/m<sup>2</sup>]</li> <li>precip: precipitation (liquid and solid) [kg/m<sup>2</sup>/s]</li> <li>QG: ground heat flux [W/m<sup>2</sup>]</li> <li>QL: latent heat flux [W/m<sup>2</sup>]</li> <li>QS: sensible heat flux [W/m<sup>2</sup>]</li> <li>snowmelt: surface melt rate [kg/m<sup>2</sup>/s]</li> <li>SWin: incoming shortwave radiation [W/m<sup>2</sup>]</li> <li>SWout: reflected shortwave radiation [W/m<sup>2</sup>]</li> <li>T2m: 2-meter temperature [K]</li> </ul> <p> </p> <p><strong>Note!</strong></p> <p>This dataset might not represent the latest version of these data. Please contact the Institute for Marine and Atmospheric Research Utrecht (imau@science.uu.nl) for information about the latest available version of these data.</p>
Along-shelf connectivity and circumpolar gene flow in Antarctic silverfish (Pleuragramma antarctica)
<p>The Antarctic silverfish (<em>Pleuragramma antarctica</em>) is a critically important forage species with a circumpolar distribution and is unique among other notothenioid species for its wholly pelagic life cycle. Previous studies have provided mixed evidence of population structure over regional and circumpolar scales. The aim of the present study was to test the recent population hypothesis for Antarctic silverfish, which emphasizes the interplay between life history and hydrography in shaping connectivity. A total of 1067 individuals were collected over 25 years from different locations on a circumpolar scale. Samples were genotyped at fifteen microsatellites to assess population differentiation and genetic structuring using clustering methods, <em>F</em>-statistics, and hierarchical analysis of variance. A lack of differentiation was found between locations connected by the Antarctic Slope Front Current (ASF), indicative of high levels of gene flow. However, gene flow was significantly reduced at the South Orkney Islands and the western Antarctic Peninsula where the ASF is absent. This pattern of gene flow emphasized the relevance of large-scale circulation as a mechanism for circumpolar connectivity. Chaotic genetic patchiness characterized population structure over time, with varying patterns of differentiation observed between years, accompanied by heterogeneous standard length distributions. The present study supports a more nuanced version of the genetic panmixia hypothesis that reflects physical-biological interactions over the life history.</p>
Results of ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century
<p>This archive provides the ice sheet model outputs produced as part of the publication "ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century", published in The Cryosphere, <a href="https://tc.copernicus.org/articles/14/3033/2020/">https://tc.copernicus.org/articles/14/3033/2020/</a></p> <p>Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, 2020.</p> <p>Contact: Helene Seroussi, Helene.seroussi@jpl.nasa.gov</p> <p>Further information on ISMIP6 and ISMIP6 Antarctica Projections can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica</p> <p>Users should cite the original publication when using all or part of the data. <br> In order to document CMIP6’s scientific impact and enable ongoing support of CMIP, users are also obligated to acknowledge CMIP6, ISMIP6 and the participating modeling groups.</p> <p>About the dataset:</p> <p>- The results are based on model output computed from the ISMIP6 native grids that vary between models. <br> - The results are calculated over the ice-covered area of Antarctica, corrected for map projection errors, ice sheet model specific densities taken into account.<br> - Results for the experiments 'exp*' are provided both as raw results and calculated as differences to the control experiment (ctrl_proj_open or ctrl_proj_std depending on the experiment). The later files are named with "minus_ctrl_proj" to indicate that the control run is substracted.<br> - Results for ctrl_proj_open, ctrl_proj_std, hist_open and hist_std are not corrected to remove the control run.</p> <p><br> ------------------------------------------------</p> <p>Directory structure:</p> <p>groupname1<br> modelname1<br> expid<br> computed_iareafl_AIS_groupname1_modelname1_expid.nc<br> computed_iareafl_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_iareagr_AIS_groupname1_modelname1_expid.nc<br> computed_iareagr_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_icearea_AIS_groupname1_modelname1_expid.nc<br> computed_icearea_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_ivol_AIS_groupname1_modelname1_expid.nc<br> computed_ivol_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_ivaf_AIS_groupname1_modelname1_expid.nc<br> computed_ivaf_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_smb_AIS_groupname1_modelname1_expid.nc<br> computed_smb_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_smbgr_AIS_groupname1_modelname1_expid.nc<br> computed_smbgr_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_bmbfl_AIS_groupname1_modelname1_expid.nc<br> computed_bmbfl_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> ...</p> <p>-------------------------------------------------</p> <p><br> Description of variables:</p> <p>icearea - ice area [m^2]<br> iareafl - floating ice area [m^2]<br> iareagr - grounded ice area [m^2]<br> ivol - ice volume [m^3]<br> ivaf - ice volume above floatation [m^3]<br> smb - spatially integrated surface mass balance [kg/s]<br> smbgr - spatially integrated surface mass balance over grounded ice [kg/s]<br> bmbfl - spatially integrated basal melt rate under floating ice (negative for melting ice) [kg/s]</p> <p>Variables per file:</p> <p>rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, in years</p> <p>[variable] - global variable integrated over the Antarctica ice sheet<br> [variable]_region_1 - variable integrated over West Antarctica<br> [variable]_region_2 - variable integrated over East Antarctica<br> [variable]_region_3 - variable integrated over the Antarctic Peninsula<br> [variable]_sector_X - variable integrated over the X sector of the Antarctic ice sheet (18 sectors, from 1 to 18)</p> <p>--------------------------------------------------</p> <p><br> Data usage notice:<br> 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.</p> <p>"We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. 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, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, 2020.</p> <p>Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A., Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J. M., Hattermann, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C. M., Morlighem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R. S., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R.: Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models, The Cryosphere, 14, 2331–2368, https://doi.org/10.5194/tc-14-2331-2020, 2020.</p>
Data from: Additive effects of pCO2 and temperature on respiration rates of the Antarctic pteropod Limacina helicina antarctica
The Antarctic pteropod, Limacina helicina antarctica, is a dominant member of the zooplankton in the Ross Sea and supports the vast diversity of marine megafauna that designates this region as an internationally protected area. Here, we observed the response of respiration rate to abiotic stressors associated with global change – environmentally relevant temperature (-0.8˚C, 4˚C) and pH treatments reflecting current-day and future modeled extremes. Sampling repeatedly over a 14-day period in laboratory experiments and using microplate respirometry techniques, we found that the metabolic rate of juvenile pteropods increased in response to high pCO2 exposure (920 µatm) at -0.8˚C, a near-ambient temperature. Similarly, metabolic rate increased when pteropods were exposed simultaneously to multiple stressors, elevated pCO2 conditions (960 µatm) and a high temperature (+4˚C). Overall, the results showed that pCO2 and temperature interact additively to affect metabolic rates in pteropods. Furthermore, we found that L. h. antarctica can tolerate acute exposure to temperatures far beyond its maximal habitat temperature. Overall, L. h. antarctica appears to be susceptible to pH and temperature stress, two abiotic stressors which are expected to be especially deleterious for ectothermic marine metazoans in polar seas.
HIWIND Balloon and Antarctica Jang Bogo FPI High Latitude Conjugate Thermospheric Wind Observations and Simulations
The data files contain thermospheric wind observations and simulations at JBS observatory and HIWIND.
Widespread seawater intrusions beneath the grounded ice of Thwaites Glacier, West Antarctica
<p>Warm water from the Southern Ocean has a dominant impact on the evolution of Antarctic glaciers and in turn on their contribution to sea level rise. Using a continuous time series of daily-repeat satellite synthetic-aperture radar interferometry data from the ICEYE constellation collected in March-June 2023, we document an ice grounding zone, or region of tidally-controlled migration of the transition boundary between grounded ice and ice afloat in the ocean, at the main trunk of Thwaites Glacier, West Antarctica, a strong contributor to sea level rise with an ice volume equivalent to a 0.6-m global sea level rise. The ice grounding zone is 6 km wide in the central part of Thwaites with shallow bed slopes, and 2 km wide along its flanks with steep basal slopes. We additionally detect irregular seawater intrusions, 5-10 cm in thickness, extending another 6 km upstream, at high tide, in a bed depression located beyond a bedrock ridge that impedes the glacier retreat. Seawater intrusions align well with regions predicted by the GlaDS subglacial water model to host a high-pressure distributed subglacial hydrology system in between lower-pressure subglacial channels. Pressurized seawater intrusions will induce vigorous melt of grounded ice over kilometers, making the glacier more vulnerable to ocean warming, and increasing the projections of ice mass loss. Kilometer-wide, widespread seawater intrusion beneath grounded ice may be the missing link between the rapid, past, and present changes in ice sheet mass and the slower changes replicated by ice sheet models. The dataset includes grounding line positions, all ICEYE radar interferograms and parameter files, files of tidal predictions and corrections for change in atmospheric pressure, and output products from the GlADS subglacial hydrology model.</p>
Phenomenological images (LL-CAM2) of Limnopolar Lake CALM site, Byers Peninsula, Livingston island, Antarctica (2023).
<p>Images acquired by a phenomenological automatic time-lapse camera (LL-CAM2) located in Limnopolar Lake permafrost and active layer monitoring site in the Limnopolar Lake basin in Byers Peninsular, Livingston island, Antarctica, in 2023.</p> <ul> <li>Location: Livingston Island, Antarctica</li> <li>Site: Limnopolar Lake sounders</li> <li>Instrument/Experiment: Phenomenological time-lapse automatic photographic camera</li> <li>Period: 2023</li> <li>Version: 1.0.0 (jpg images as they were obtained from camera, without processing)</li> <li>Camera/manufacturer: CC5MPX by Campbell Scientific Inc.</li> <li>Resolution/Type/Format: 5Mpixels in RGB in jpg files</li> <li>Frequency: 3 images per day at 14h, 15h and 16h GMT</li> <li>Site: Close to Limnopolar Lake CALM site, in Limnopolar Lake basin (Byers Peninsula), Livingston island, Antarctica.</li> <li>Latitude: -62.6485556 </li> <li>Longitude:-61.1013611</li> <li>Elevation: 77 m</li> <li>Initial dataset date/time: February 8, 2023 14:00h GMT</li> <li>Final dataset date/time: February 10, 2024 16:00h GMT</li> <li>Gaps: None</li> <li>Notes: <ul> <li>File code: LIV_LL_CAM2_2023_v100</li> <li>Location code: LIV</li> <li>Site code: LL</li> <li>Instrument code: CAM2</li> <li>Folders: 13</li> <li>Files: 1103</li> <li>Other files: none</li> <li>Folder names structure: year_month</li> <li>Datafiles names structure: Site_camera_year_month_day_hour_minute_second.jpg</li> </ul> </li> </ul>
Supplementary Data for A large-scale transcontinental river system crossed West Antarctica during the Eocene
<p>The file contains supplementary figures to the paper "A large-scale transcontinental river crossed West Antarctica during the Eocene", by Max Zundel, Cornelia Spiegel, Chris Mark, Ian Millar, David Chew, Johann Klages, Karsten Gohl, CD Hillenbrand, Yani Najman, Ulrich Salzmann, Werner Ehrmann, Jürgen Titschack, Thorsten Bauersachs, Gabi Uenzelmann-Neben, Torsten Bickert, Jule Müller, Frank Lisker, Steve Bohaty, Gerd Kuhn, and the Science Team of Polarstern Expedition PS104, published in Science Advances</p> <p><a name="_Hlk153790947"></a></p>
Figure 1 in Revisiting Potter Cove, King George Island, Antarctica, 12 years later: new observations of marine benthic diatoms
Figure 1: Map of sampling locations in Potter Cove, King George Island, Antarctica.
Monthly RACMO2.4p1 data for Antarctica (11 km) for SMB, SEB and near-surface variables (1979-2023)
<p>This data set includes the surface mass balance (SMB) components, surface energy budget (SEB) variables and near-surface climate variables as is simulated by the regional atmospheric climate model RACMO2.4p1 over Antarctica from January 1979 to December 2023. At the lateral boundaries, RACMO is forced with ERA5 multilevel data of wind speed, temperature, humidity and pressure. At the sea-surface boundary, sea ice extent and sea surface temperature are also forced by ERA5. RACMO is run with 40 vertical levels and on an 11 km horizontal resolution grid. The domain includes Antarctica, the Drake passage and parts of Patagonia.</p> <p>Variables are monthly-averaged (named monthlyA), monthly-accumulated (named monthlyS), or part of the mask file. Variables ending with ‘gl’ are for glaciated surfaces only. Integrating a variable over the doman (e.g., calculating the SMB in Gigaton per month) requires multiplying the variable with the grid cell area (‘Area’) and ice mask (‘IceMask’) fields, both can be found in the ANT11_masks.nc file. The ice mask is a fractional field since RACMO2.4p1. If limiting the integration to the ice sheet only, the considered variable has to be multiplied with the ‘Icesheet_Only’ field that is also available in the mask file, in addition to multiplying it with the grid cell area and ice mask. The mask file includes more fields, which are also listed below. Unlisted variables and listed variables with a higher temporal resolution are available on request. This data set includes the following variables: </p> <p><strong>Surface mass balance (SMB) variables (in kg m<sup>-2 </sup>mo<sup>-1</sup> or mm water equivalent mo<sup>-1</sup>):<br></strong></p> <p>smbgl: (Specific) Surface mass balance</p> <p>mltgl: Surface snow melt production</p> <p>rfrzgl: Refreezing of liquid water</p> <p>sf: Solid precipitation</p> <p>pr: Total precipitation (to determine rain: ra = pr – sf)</p> <p>prgl: Total precipitation for glaciated surfaces only</p> <p>totrunoff: (Near-)surface meltwater runoff on glaciated surfaces</p> <p>subltot: Sublimation of snow (including sublimation of suspended snow in the atmosphere caused by blowing snow). Negative values are sublimation, positive values are riming.</p> <p>sndivgl: Erosion of blowing snow</p> <p> </p> <p><strong>Surface Energy Budget (SEB) variables (in J m<sup>-2</sup>): <br></strong></p> <p>To convert to W m<sup>-2</sup>, divide by the number of seconds in a month.</p> <p>rsds: Downwelling shortwave radiation at the surface</p> <p>rsusgl: Upwelling shortwave radiation at the surface</p> <p>rlds: Downwelling longwave radiation at the surface</p> <p>rlusgl: Upwelling longwave radiation at the surface</p> <p>hfssgl: Sensible heat flux, defined positive towards the surface</p> <p>hflsgl: Latent heat flux, defined positive toward the surface</p> <p>gbot: Ground heat flux for glaciated surfaces</p> <p> </p> <p><strong>Other variables:</strong></p> <p>tas: Near-surface air temperature (2 m above the surface)</p> <p>ts: Surface temperature (skin temperature)</p> <p>huss: Near-surface specific humidity (2 m above the surface</p> <p>hurs: Near-surface relative humidity (2 m above the surface)</p> <p>ps: Surface pressure</p> <p>sfcwind: Near-surface wind speed</p> <p>u10m: Zonal wind speed at 10 m above the surface</p> <p>v10m: Meridional wind speed at 10 m above the surface</p> <p><strong> </strong></p> <p><strong>Fields available in the mask file:</strong></p> <p>lat: Latitude</p> <p>lon: Longitude</p> <p>lat_bnds: Latitude of the four corners of each grid point</p> <p>lon_bnds: Longitude of the four corners of each grid point</p> <p>IceMask: Ice mask fraction (grounded ice and ice shelves)</p> <p>LSM: Land-sea mask (0 or 1)</p> <p>Geopotential: Geopotential height</p> <p>Topography: Topographic height of the surface</p> <p>Area: Surface area of each grid point</p> <p>Basins: Drainage basins following the IMBIE convention</p> <p>Icesheet_Only: Mask showing which grid points are considered to be part of the Antarctic ice sheet (0 or 1)</p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017
<p>The maximum extent of supraglacial lakes and channels in West Antarctica and the Antarctic Peninsula in January 2017 was produced by a Dual-NDWI (Normalised Difference Water Index) approach with thresholds. >2000 individual scenes were captured by Sentinel-2 (S2, filename: TNNXXX_YYYYMMDD) and Landsat-8 (L8, filename: LC08_L1GT_NNNNNN_YYYYMMDD_YYYYMMDD_01_T2) satellite sensors during the entire month of January 2017. To obtain maximum coverage on the cloudy Antarctic Peninsula, the time period is extended to February 10, 2017 over this region.</p> <p>This dataset consists of the maximum extent of supraglacial hydrological activity during January 2017 and detailed 10,478 supraglacial features (10,223 lakes and 255 channels), with cumulative area 119.4 square km in total on the West Antarctic ice sheet and Antarctic Peninsula. In addition to the final product, the supraglacial hydrological features from both sensors (23,389 polygons for S2 and 17,571 polygons for L8) overlapping the final map are included in supplementary datasets. The supraglacial lake and channel polygons are available as digital GIS, Geographic Information System, shapefiles (.shp) and GeoJSON files as well as Google Earth format (.kmz). The code used to produce the lake and channel dataset for each sensor (S2 and L8) is implemented using Python, and can be accessed on Zenodo (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0</a>) . Landsat-8 and Sentinel-2 imagery are freely available at (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0</a>) and (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0</a>), respectively.</p> <p>The products provide a scientific benchmark to monitor the development of these features in a warming climate, and thus enhancing our capability to predict the calving and collapse of any ice shelves in the future. The results provide a baseline for future monitoring of supraglacial hydrology and can be particularly useful to train supervised machine learning algorithms. The lake and channel dataset will be valuable as training data for pixel-based or object-based approaches to map large-scale features automatically using machine learning. This dataset can also provide an a-priori lake distribution for studies incorporating synthetic-aperture radar, SAR and other sensors and platforms.</p>
Nitrate δ15N values and surface mass balance reconstructions from East Antarctica
<p>Geographic information, surface mass balance (SMB) data, and sub-photic zone (>0.3 m) nitrate concentration and nitrogen isotopic composition (δ15NNO3) for 135 sites across East Antarctica. This database was used to examine and define the relationship between δ15NNO3 and SMB in Antarctica as part of the SCADI (Snow Core Accumulation from Delta-15N Isotopes) and EAIIST (East Antarctic International Ice Sheet Traverse) projects. Of these 135 sites, 92 are newly reported here while the other site data were previously published and are cited accordingly. Snow bearing nitrate was sampled from snow pits and firn/ice cores at different dates depending on the original scientific campaign, but predominately between 2010 and 2020, with the earliest sampling occurring in 2004. Nitrate was later extracted from the snow, concentrated, and analyzed for δ15NNO3. Surface mass balance data comes from a combination of previous ground-based observations (e.g., stakes, ice core data) and the output from Modèle Atmosphérique Régional version 3.6.4 with European Centre for Medium-Range Weather Forecasts “Interim” re-analysis data (ERA-interim) data, adjusted for observed model SMB biases. Elevation data were extracted from the Reference Elevation Model of Antarctica (REMA, <a href="https://doi.org/10.5194/tc-13-665-2019">https://doi.org/10.5194/tc-13-665-2019</a>).</p> <p>Also contains nitrate concentration and isotopic (δ15NNO3) data, ice density, and surface mass balance estimates from the ABN1314-103 ice core. This 103 m long core was drilled beginning on 07 January 2014 as one of three ice cores at Aurora Basin North, Antarctica (-71.17, 111.37, 2679 m.a.s.l), in the 2013-2014 field season. The age-depth model for ABN1314-103 was matched through ion profiles from an annually-resolved model (ALC01112018) originally developed for one of the other ABN cores through seasonal ion and water isotope cycles and constrained by volcanic horizons. Each 1 m segment of the core was weighed and measured for ice density calculations, and then sampled for nitrate at 0.33 m resolution. Nitrate concentrations were taken on melted ice aliquots with ion chromatography, while isotopic analysis was achieved through bacterial denitrification and MAT 253 mass spectrometry after concentrating with anionic resin. Using the density data and the age-depth model’s dates for the top and bottom of each 1 m core segment, we reconstructed a history of surface mass balance changes as recorded in ABN1314-103. Additionally, we also estimated the effect of upstream topographic changes on the ice core’s surface mass balance record through a ground penetrating radar transect that extended 11.5 km against the direction of glacial ice flow. The modern SMB changes along this upstream transect were linked to ABN1314-103 core depths by through the local horizontal ice flow rate (16.2 m a-1) and the core’s age-depth model, and included here for comparative analysis. </p>
Seismic catalogs of the 2020-2021 seismic swarm at the Bransfield Strait, Antarctica
<p>Three seismic catalogs are available:</p> <p>1. Single station catalog for station JUBA based on the Lassie software<br> 2. Moment tensor inversion catalog with moment tensor classification<br> 2. Template matching catalog</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.