(@)Haywaa [LinkedIn]: "A study on the Greenland ice sheet... predicts a potential 3-foot sea rise by 2100 from Greenland's melt." #Crevasses #SeaLevelRise #Glaciology https://lnkd.in/gmFRniWD
(@)Haywaa [LinkedIn]: "A study on the Greenland ice sheet... predicts a potential 3-foot sea rise by 2100 from Greenland's melt." #Crevasses #SeaLevelRise #Glaciology https://lnkd.in/gmFRniWD
Well that's a nice message to receive on a Friday afternoon, I feel like I should pack up for the weekend now. Except of course, the wheels of #science keep turning...
Congratulations to @agrinsted who did the vast majority of the work on our first #PRECISE_NNF paper
EDIT: To add a link in case you want to spend a quiet half hour reading about the fracture properties of ice
Abstract. Ice fractures when subject to stress that exceeds the material failure strength. Previous studies have found that a von Mises failure criterion, which places a bound on the second invariant of the deviatoric stress tensor, is consistent with empirical data. Other studies have suggested that a scaling effect exists, such that larger sample specimens have a substantially lower failure strength, implying that estimating material strength from laboratory-scale experiments may be insufficient for glacier-scale modeling. In this paper, we analyze the stress conditions in crevasse onset regions to better understand the failure criterion and strength relevant for large-scale modeling. The local deviatoric stress is inferred using surface velocities and reanalysis temperatures, and crevasse onset regions are extracted from a remotely sensed crevasse density map. We project the stress state onto the failure plane spanned by Haigh–Westergaard coordinates, showing how failure depends on mode of stress. We find that existing crevasse data are consistent with a Schmidt–Ishlinsky failure criterion that places a bound on the absolute value of the maximal principal deviatoric stress, estimated to be 158±44 kPa. Although the traditional von Mises failure criterion also provides an adequate fit to the data with a von Mises strength of 265±73 kPa, it depends only on stress magnitude and is indifferent to the specific stress state, unlike Schmidt–Ishlinsky failure which has a larger shear failure strength compared to tensile strength. Implications for large-scale ice flow and fracture modeling are discussed.
View of the leading edge of the Perito Moreno Glacier in Patagonia Argentina, seen from a lake at its edge. See more here: https://joan-carroll.pixels.com/featured/edge-of-perito-moreno-glacier-patagonia-argentina-joan-carroll.html
#glacier #argentina #patagonia #jagged #rugged #ice #crevasses #towering #AYearForArt #BuyIntoArt #giftideas @joancarroll
Attached: 1 image How great a stress can a glacier support before a crevasse forms? In our new study we investigate the stress conditions in Greenland crevasse fields. It turns out it depends on the mode of stress. preprint here: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1957/
"A team of scientists from the University of Leeds and University of Bristol have adapted an AI algorithm originally developed to identify cells in microscope images to spot crevasses forming in the ice from satellite images. Crevasses are indicators of stresses building-up in the glacier".
#AI #glaciers #Antarctic #crevasses
https://www.eurekalert.org/news-releases/975749
Scientists have developed artificial intelligence techniques to track the development of crevasses - or fractures - on the Thwaites Glacier Ice Tongue in west Antarctica. A team of scientists from the University of Leeds and University of Bristol have adapted an AI algorithm originally developed to identify cells in microscope images to spot crevasses forming in the ice from satellite images. Crevasses are indicators of stresses building-up in the glacier. Thwaites is a particularly important part of the Antarctic Ice Sheet because it holds enough ice to raise global sea levels by around 60 centimetres and is considered by many to be at risk of rapid retreat, threatening coastal communities around the world. Use of AI will allow scientists to more accurately monitor and model changes to this important glacier.