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1,337 results for “antarctica”
Figure 1 in Osteology of Cryolophosaurus ellioti (Dinosauria: Theropoda) from the Early Jurassic of Antarctica and implications for early theropod evolution
Figure 1. Generalized map of Antarctica (A), with inset maps showing the Central Transantarctic Mountains (B), and the Beardmore Glacier area where the Mount Kirkpatrick dinosaur site is located (C). D, generalized stratigraphy and age of rock units in the Beardmore Glacier area.
Figure 8 in Osteology of Cryolophosaurus ellioti (Dinosauria: Theropoda) from the Early Jurassic of Antarctica and implications for early theropod evolution
Figure 8. Portions of the left jugal, postorbital and squamosal of Cryolophosaurus ellioti in medial aspect (A), and interpretive line drawing (B). These elements have been split in half from the left side of the skull.
Water tagging experiments for the interpretation of deuterium excess in Antarctica
<p>These simulation results are from the water-tagging experiments in the manuscript "Sublimation origin of negative deuterium excess observed in snow and ice samples from McMurdo Dry Valleys and Allan Hills Blue Ice Areas, East Antarctica" submitted to the Journal of Geophysical Research-Atmospheres. The file tag_antactic_names.pdf shows the abbreviations of all tagging region names. </p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Landsat-8 Group 2
<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) and Landsat-8 (L8) 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. 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> <p>Alongside Landsat-8 Group 1, this dataset provides the 17,571 polygons from L8 imagery.</p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Sentinel-2 Group 2
<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) and Landsat-8 (L8) 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. 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> <p>Alongside Sentinel-2 Group 1, this dataset provides the 23,389 polygons from S2 imagery.</p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Landsat-8 Group 1
<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) and Landsat-8 (L8) 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. 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> <p>Alongside Landsat-8 Group 2, this dataset provides the 17,571 polygons from L8 imagery.</p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Sentinel-2 Group 1
<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) and Landsat-8 (L8) 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. 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> <p>Alongside Sentinel-2 Group 2, this dataset provides the 23,389 polygons from S2 imagery.</p>
Vertically pointing doppler radar profiles (24 GHz Metek MRR-2) at Mario Zucchelli Station (Terra Nova Bay, Antarctica), aggregated to 5min, monthly netCDF archive
<p>Vertical profiles along the first kilometre of atmosphere above the ground (from 105 to 1050 m AGL) of equivalent radar reflectivity factor (Ze), Doppler velocity (W) and Doppler spectral width (Sw) from a 24-GHz vertically pointing Micro Rain Radar MRR-2 by METEK GmbH positioned at Mario Zucchelli Station (Terra Nova Bay, Antarctica).</p> <p>Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/api/records/6fe32f1f-247e-493d-9cd3-88714e5b38ef" target="_blank" rel="noopener">https://antarcticdatacenter.cnr.it/geonetwork/srv/api/records/6fe32f1f-247e-493d-9cd3-88714e5b38ef</a></p> <p>--------------------------------------------------------------------</p> <p>Example of netCDF file structure: </p> <h2>File "MZS_MRR_MeK_201912_5min.nc"</h2> <pre><strong> dimensions</strong>: <em>range </em>= 31; <em>time </em>= UNLIMITED; // (8928 currently) <strong>variables</strong>: float <em>Ze</em>(range=31, time=8928); :description = "Equivalent reflectivity factor relative to the most significant peak, dealiased, 5min non-logarithmic average. NaN means clear sky at the specified height."; :units = "dBZ"; :_ChunkSizes = 31U, 1U; // uint long <em>time_UTC</em>(time=8928); :description = "Measurement time. Timestamp indicates the end of the aggregation interval, e.g. 01-Mar-2020 00:05:00 represents the average of the variables between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float <em>W</em>(range=31, time=8928); :description = "Mean Doppler Velocity of the most significant peak, dealiased, 5min average. Value not available in clear-sky conditions."; :units = "m s^-1"; :_ChunkSizes = 31U, 1U; // uint float <em>height</em>(range=31, time=8928); :description = "Height above instrument."; :units = "m"; :_ChunkSizes = 31U, 1U; // uint float <em>spectralWidth</em>(range=31, time=8928); :description = "Doppler Spectral Width of the most significant peak, dealiased, 5min average. Value not available in clear-sky conditions."; :units = "m s^-1"; :_ChunkSizes = 31U, 1U; // uint // <strong>global attributes</strong>: :<em>title </em>= "Micro rain radar data processed with IMProToo (Maahn, M. and Kollias, P., 2012), aggregated to 5min, monthly netCDF archive."; :<em>comment </em>= "IMProToo has been developed for improved snow measurements. Note that this data has been processed regardless of precipitation type."; :<em>time_label </em>= "Dec 2019"; :<em>source </em>= "Micro Rain Radar 2 (MRR-2), METEK GmbH, at MZS (Antarctica), frequency: 24 GHz, power: 50 mW, antenna diameter: 60 cm [https://metek.de/product/mrr-2/]"; :<em>institution </em>= "CNR-ISAC, Rome (IT)"; :<em>contact_person </em>= "Luca Baldini, CNR-ISAC, Rome (IT), l.baldini@isac.cnr.it"; :<em>location </em>= "Mario Zucchelli Station (Terra Nova Bay, Antarctica, 74°42\'S, 164°07\'E, 15 m a.s.l.)"; :<em>author </em>= "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :<em>creation_date </em>= "22-Oct-2024 17:40:46 UTC"; :<em>coverage </em>= "Monthly coverage (Dec 2019): 100 %"; :<em>time_resolution </em>= "5 minutes"; :<em>history </em>= "Created with IMProToo v0.107 [https://github.com/maahn/IMProToo], aggregated to 5 minutes temporal resolution with an average of the 1-minute values if least 3 out of 5 are not NaN."; </pre>
Supplementary data for 'Melting and refreezing in an ice shelf basal channel at the grounding line of the Kamb Ice Stream, West Antarctica' Whiteford et al 2022
<p>These data are described in detail by 'Melting and refreezing in an ice shelf basal channel at the grounding line of the Kamb Ice Stream, West Antarctica' Whiteford et al 2022.</p> <p>'ApRES dataset.zip' contains raw ApRES data and processed results from a spatial survey of basal mass balance, detailed in Sections 2.2.4 and 3.2.2 of the above paper. README.md describes the file contents.</p> <p>'radar_dataset.tar.gz' contains raw data from a low frequency radar survey profiling ice thickness, detailed in Section 2.2.1 of the above paper.</p> <p> </p> <p>'channel_base_surface_map' contains six files. *_Ice_thickness is raster data of an estimation of ice thickness in the area. This is produced through processing radar data 'radar_dataset.tar.gz' and interpolation, described in Sections 2.2.2 and 3.1.2. *_REMA_surf is raster data of the ice surface, sampled a REMA strip from 9 November 2016 (Howat 2019). Ice_base is raster data calculated by subtracting the ice thickness from this surface. *_x_grid and *_y_grid are the x and y UTM coordinates accompanying the raster data, in Antarctic Polar Stereographic projection. *_extent is the x and y extent of the area covered by raster data.</p> <p>Reference:</p> <p>Howat, I. M., Porter, C., Smith, B. E., Noh, M.-J., & Morin, P. (2019). The Reference Elevation Model of Antarctica. Cryosphere, 13 (2)</p>
Variable-resolution CESM2 over Antarctica (ANTSI): Monthly outputs used for evaluation.
<p>Funding generously provided by NSF Award 1952199 as well as additional support from NCAR in the creation of the dataset and the use of VRCESM. </p> <p>Included are outputs from an AMIP-style (data-driven ocean and sea ice) simulation of Variable-Resolution CESM2 from 1979-2015. Resolution is 1° globally with a refined 0.25° resolution over the Southern Ocean as well over the Antarctic ice sheet. Outputs are at a monthly timescale, and include those variables relevant for evaluation.</p> <p>Each netcdf file ends with several relevant tags to indicate <source>.<output deisgnator>.<variable>.<time period>.nc. Atmospheric variables are labeled with "cam" whereas ice sheet variables are labeled with "clm2".</p> <p>clm2.h0.SNOW.198901-199812.nc</p> <p>cam.h1.Q.198901-199812.nc</p> <p>Variables are described for CESM2 (see NCAR documentation for clm and cam)</p> <p>Variables included for cam include FLDS, FLNS, CLDICE, CLDLIQ, LHFLX, PRECC, PRECL, PRECSC, PS,Q, SHFLX, U, V, Z3</p> <p>Variables included for clm2 include FIRA, FIRE, FLDS, FSDS, FSH, QICE, QRUNOFF, QSNOMELT, QSOIL, RAIN, SNOW</p> <p> </p>
200-years ice core bromine reconstruction at Dome C (Antarctica): observational and modelling results
<p>Bromine enrichment (Br<sub>enr</sub>) has been proposed as an ice core proxy for past sea-ice reconstruction. Understanding the processes that influence bromine preservation in the ice is crucial to achieve a reliable interpretation of ice core signals and to potentially relate them to past sea ice variability. Here, we present a 210 years bromine record that sheds light on the main processes controlling bromine preservation in the snow and ice at Dome C, East Antarctic plateau. Using observations alongside a modelling approach, we demonstrate that the bromine signal is preserved at Dome C and it is not affected by the strong variations in ultraviolet radiation reaching the Antarctic plateau due to the stratospheric ozone hole. Based on this, we investigate whether the Dome C Br<sub>enr</sub> record can be used as an effective tracer of past Antarctic sea ice. Due to the limited time window covered by satellite measurements and the low sea-ice variability observed during the last 30 years in East Antarctica, we cannot fully validate Br<sub>enr </sub>as an effective proxy for past sea-ice reconstructions at Dome C.</p> <p>In this database, annual averaged data for Br, Na and Br<sub>enr</sub> for the shallow ice core are provided.</p> <p> </p>
Dataset - "Ice core evidence for a 20th century increase in surface mass balance in coastal Dronning Maud Land, East Antarctica"
<p>We provide in this dataset the isotopic and ionic records from a 120 m-long ice core drilled at the summit of Derwael ice rise (70°14'44.88'' S, 26°20'5.64'' E) situated in coastal Dronning Maud Land, East Antarctica. Ions concentrations (Na<sup>+</sup>, MSA, Cl<sup>-</sup>, SO<sub>4</sub><sup>2-</sup> and NO<sub>3</sub><sup>-</sup>) and water stable isotopes (δ<sup>18</sup>O, δD and d-excess) are presented for the top 103 meters (corresponding to 1815 and the Tambora eruption) with a continuous record for the water stable isotopes and discontinuous sections for ions concentrations. A complementary database “Annual layer thicknesses and age-depth (oldest estimate) of Derwael Ice Rise (IC12), Dronning Maud Land, East Antarctica” with age model and annual layer thickness is available at https://doi.org/10.1594/PANGAEA.857574 and the companion paper is Philippe et al., 2016 (“Ice core evidence for a 20th century increase in surface mass balance in coastal Dronning Maud Land, East Antarctica”, https://doi.org/10.5194/tc-10-2501-2016).</p>
FIG. 8 in First report of diversity of Cyanobacteria of Broknes Peninsula of Larsemann Hills, East Antarctica
FIG. 8. — Cyanoprokaryotes of Broknes Peninsula. A, Chroococcus minutus (Kütz.) Nägeli; B, Gloeocapsa biformis Ercegovic; C, Cyanosarcina burmensis Skuja, D, Oscillatoria anguina (Bory) Gomont; E, Oscillatoria curviceps C.Agardh ex Gomont; F, Oscillatoria limosa C.Agardh ex Gomont; G, Planktothrix rubescens (DC. ex Gomont) Anagn. & Komárek; H, Lyngbya confervoides C.Agardh ex Gomont; I, Phormidium ambiguum Gomont; J, Phormidium bekesiense (I.Kiss) K.Kiss,; K, Oscillatoria jenensis G.Schmid; L, Phormidium subfuscum Kütz. ex Gomont; M, Oscillatoria tenuis C.Agardh ex Gomont; N, Aulosira fritschii Bharadwaja; O, Coleospermum goeppertianum (goppertianum) Kirch. ex Frank; P, Stigonema mesentericum Geitler. Scale bars: A, N, O, 30 µm; B, C, F, I, L, 10 µm; D, E, G, H, J, K, M, P, 20 µm.
Physico-chemical properties of the top 120 m of two ice cores in Dronning Maud Land (East Antarctica)
<p>We provide in this dataset the complete records from the top 120 m of two ice cores drilled on two adjacent ice rises (Hammarryggen and Lokeryggen, ~100 km apart) located in coastal Dronning Maud Land, Antarctica. Density, water stable isotopes (δ<sup>18</sup>O, δD and d-excess), ions concentrations (Na<sup>+</sup>, K<sup>+</sup>, Mg<sup>+</sup>, Ca<sup>+</sup>, MSA, Cl<sup>-</sup>, SO<sub>4</sub><sup>2-</sup> and NO<sub>3</sub><sup>-</sup>), and continuous electrical conductivity measurement (ECM) records, as well as age models and resulting surface mass balance (SMB) are presented here. These ice cores are dated back to the end of the eighteenth century (CE 1793 ± 3 years for FK17and 1780 ± 5 years for TIR18) by manual and automatic multivariable layer counting and volcanic tie points validation. The SMB is calculated after correction for vertical strain rates.</p> <p>A detailed description of the measurement procedures, data treatments and uncertainties can be found in: Wauthy S., Tison, J.-L., Inoue, M., El Amri, S., Sun, S., Claeys, P., and Pattyn, F.: Physico-chemical properties of the top 120 m of two ice cores in Dronning Maud Land (East Antarctica): an open window on spatial and temporal regional variability of environmental proxies, submitted to Earth System Science Data (ESSD).</p> <p>These new records can be compared to those from other coastal ice cores in DML and to regional or global model results, and represent a great opportunity for further studies addressing the mechanisms behind SMB spatial and temporal variability in East Antarctica.</p> <p>The study was funded in the framework of the Mass2Ant project, contract n° BR/165/A2 by the Belgian Science Policy BELSPO.</p>
Videos of 1966-1978 Satellite Images over Antarctica
<p>Videos of satellite images provided by Campbell (2019). </p> <p>vis_norm_54d.mp4 - images in visible spectrum South of 54 degrees South. </p> <p>vis_norm_remapped_reg_54d_labeled.mp4 - processed version of the same images. Missing sections were manually labeled and all images were registered such that the longitude and latitude lines match across all images.</p> <p>Campbell, G. (2019). NOAA Polar Orbiting Satellite (POES) Global Visible<br> and Infrared Band Data from ESSA (1966-1972) and NOAA (1972-1978)<br> Satellites. Technical report, NOAA National Centers for Environmental<br> Information.</p>
Whole genome sequence and annotation dataset of rare actinobacteria, Barrientosiimonas humi gen. nov., sp. nov. 39T from Antarctica
<p>The present data files are the source files of the annotation output from the whole genome sequencing of rare actinobacteria, <em>Barrientosiimonas humi gen. nov., sp. nov.</em> 39<sup>T</sup> from Antarctica.</p> <p>The dataset of the whole-genome sequence of <em>B. humi</em> had been deposited in European Nucleotide Archive (ENA) repository under the accession number PRJEB44986 / ERP129097, direct URL to data:<strong> </strong><a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB44986">https://www.ebi.ac.uk/ena/browser/view/PRJEB44986</a></p>
Fifty years of instrumental surface mass balance observations at Vostok Station, central Antarctica
<p>The database of snow buildup, density and accumulation rate values as observed at the accumulation-stake farms in the vicinity of Vostok station (central East Antarctica) since January 1970.</p> <p>The reference for the data: Ekaykin A.A., Lipenkov V.Ya., Tebenkova N.A. Fifty years of instrumental surface mass balance observations at Vostok Station, central Antarctica. - J. of Glaciology, 2023, 1–13. https://doi.org/10.1017/jog.2023.53.</p> <p> </p>
Data set: Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022
<p><strong>Version 2:</strong></p> <p><strong>Updates from version 1: Monthly, daily, and hourly dist-PDD and uni-PDD outputs have been added.</strong></p> <p><strong>https://doi.org/10.5194/tc-17-3667-2023</strong></p> <p> </p> <p>Version 1:</p> <p>This dataset accompanies Zheng et al. (2023): Statistically parameterizing and evaluating a positive degree-day<br> model to estimate surface melt in Antarctica from 1979 to 2022, The Cryosphere.</p> <p>This dataset contains annual PDD model output.</p>
Soil texture dataset from the publication: "Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula'
<p>Clay, silt and sand distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The coefficient of variation and quantile data represent the spatial uncertainty of the predictions. For more information about the methodology used, users are referred to the article: </p> <p>Siqueira, R.G., Moquedace, C.M., Francelino, M.R., Schaefer, C.E.G.R., Fernandes-Filho, E.I., 2023. Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula. Geoderma 432, 116405. https://doi.org/10.1016/j.geoderma.2023.116405</p> <p>The .zip file has the following folders:</p> <p>1) soil_texture_antarctica: soil texture information containing clay, silt and sand contents</p> <p>2) soil_texture_coefficient_variation: uncertainty from the coefficient of variation of the soil texture prediction</p> <p>3) soil_texture_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil texture prediction</p> <p>4) soil_texture_quantile05: quantile 5% of the soil texture prediction</p> <p>5) soil_texture_quantile95: quantile 95% of the soil texture prediction</p>
Soil chemistry dataset from the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica"
<p>Bases sum, H+Al (potential acidity), pH, phosphorous, remaining P (P-rem), sodium and total organic carbon distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The quantile and prediction interval data represent the spatial uncertainty of the predictions.</p> <p>As soon as the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica" is published, the paper will be cited here. </p> <p>The .zip file contains the following folders:</p> <p>1) soil_chemistry_antarctica: data containing the soil chemical attributes distribution</p> <p>2) soil_chemistry_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil attributes prediction</p> <p>4) soil_texture_quantile05: quantile 5% of the soil attributes prediction</p> <p>5) soil_texture_quantile95: quantile 95% of the soil attributes prediction</p>
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