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Volume 1, December
 
 

Glacies, Volume 1, Issue 1 (September 2024) – 5 articles

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23 pages, 4759 KiB  
Article
Crafting Glacial Narratives: Virtual Exploration of Alpine Glacial and Periglacial Features in Preston Park, Glacier National Park, Montana, USA
by Jacquelyn Kelly, Dianna Gielstra, Lynn Moorman, Uwe Schulze, Niccole V. Cerveny, Johan Gielstra, Rohana J. Swihart, Scott Ramsey, Tomáš J. Oberding, David R. Butler and Karen Guerrero
Glacies 2024, 1(1), 57-79; https://doi.org/10.3390/glacies1010005 - 6 Sep 2024
Viewed by 1740
Abstract
Virtual learning environments (VLEs) in physical geography education offer significant potential to aid students in acquiring the essential skills for the environmental interpretation of glacial and periglacial environments for geoscience careers. Simulated real-world field experiences aim to help the student evaluate landscapes for [...] Read more.
Virtual learning environments (VLEs) in physical geography education offer significant potential to aid students in acquiring the essential skills for the environmental interpretation of glacial and periglacial environments for geoscience careers. Simulated real-world field experiences aim to help the student evaluate landscapes for natural hazards, assess their intensity, and translate and communicate this information to various stakeholders in human systems. The TREE-PG framework and VRUI model provide a philosophical and practical foundation for VLE architects, aiming to cultivate students’ knowledge, skills, and identity as geoscientists, specifically as physical geographers and geomorphologists. These frameworks emphasize the importance of translating scientific knowledge from physical features into engaging, accessible online lessons, exemplified by landscapes like those in Glacier National Park, Montana. Open-source software and open educational resources (OERs) can broaden access and incorporate diverse perspectives in these experiences, which are necessary to address the impacts of vulnerable communities to global deglaciation. Designing and creating virtual proxies of field-based education may help address issues associated with inclusion and belonging within geoscience disciplines to connect all students with dynamic physical environments beyond the classroom. Ethical AI approaches and discipline-specific repositories are needed to ensure high-quality, contextually accurate VLEs. AI’s tendency to produce output necessitates using domain-specific guardrails to maintain relevance and precision in virtual educational content. Full article
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22 pages, 2254 KiB  
Article
Bootstrap Methods for Bias-Correcting Probability Distribution Parameters Characterizing Extreme Snow Accumulations
by Kenneth Pomeyie and Brennan Bean
Glacies 2024, 1(1), 35-56; https://doi.org/10.3390/glacies1010004 - 7 Aug 2024
Viewed by 1466
Abstract
Accurately quantifying the threat of collapse due to the weight of settled snow on the roof of a structure is crucial for ensuring structural safety. This quantification relies upon direct measurements of the snow water equivalent (SWE) of settled snow, though most weather [...] Read more.
Accurately quantifying the threat of collapse due to the weight of settled snow on the roof of a structure is crucial for ensuring structural safety. This quantification relies upon direct measurements of the snow water equivalent (SWE) of settled snow, though most weather stations in the United States only measure snow depth. The absence of direct load measurements necessitates the use of modeled estimates of SWE, which often results in the underestimation of the scale/variance parameter of the distribution of annual maximum SWE. This paper introduces a novel bias correction method that employs a bootstrap technique with regression-based models to calibrate the variance parameter of the distribution. The efficacy of this approach is demonstrated on real and simulated datasets. The findings reveal varied levels of success, with the efficacy of the proposed approach being inherently dependent on the quality of the selected regression-based model. These findings demonstrate that integrating our approach with a suitable regression-based model can produce unbiased or nearly unbiased annual maximum SWE distribution parameters in the absence of direct SWE measurements. Full article
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16 pages, 1229 KiB  
Article
Northern Hemisphere Glaciation: Its Tectonic Origin in the Neogene Uplift
by Hsien-Wang Ou
Glacies 2024, 1(1), 19-34; https://doi.org/10.3390/glacies1010003 - 21 Jul 2024
Cited by 2 | Viewed by 1078
Abstract
The Earth has cooled since the early Pliocene, which was punctuated by accelerated cooling indicative of thresholds. I posit that the cooling was initiated when the Neogene uplift of the Tibetan highland caused it to ice over, augmenting the albedo. I formulate a [...] Read more.
The Earth has cooled since the early Pliocene, which was punctuated by accelerated cooling indicative of thresholds. I posit that the cooling was initiated when the Neogene uplift of the Tibetan highland caused it to ice over, augmenting the albedo. I formulate a minimal warm/cold/Arctic box model to test this hypothesis and prognose the Pliocene climate. In particular, based on model physics, I discern three thermal thresholds as Pliocene cools: (1) when the Arctic temperature falls below the marking temperature of the ice front, the East Greenland ice sheet would descend to the sea level and calve into the Nordic Seas; (2) when the Arctic temperature cools to the freezing point, the ice sheet would form and expand over circum-Arctic lowlands to cause a massive deposition of ice-rafted debris marking Northern Hemisphere glaciation (NHG); (3) when glacial state persists through low eccentricity, it would cause a transition from obliquity- to eccentricity-dominated glacial cycles. Aligning these thresholds with the observed ones around 3.5, 2.7, and 1 million years ago, the model produces a temporal evolution of the Pliocene temperature as well as its driving albedo change. Since the latter can be accommodated by the observed one, it supports the Neogene uplift as the tectonic origin of NHG. Full article
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2 pages, 504 KiB  
Editorial
Glacies—A New Open Access Journal
by Steven R. Fassnacht
Glacies 2024, 1(1), 17-18; https://doi.org/10.3390/glacies1010002 - 6 Jun 2024
Viewed by 1348
Abstract
Glacies means “the ice” in Latin [...] Full article
16 pages, 4649 KiB  
Article
Location Dictates Snow Aerodynamic Roughness
by Steven R. Fassnacht, Kazuyoshi Suzuki, Masaki Nemoto, Jessica E. Sanow, Kenji Kosugi, Molly E. Tedesche and Markus M. Frey
Glacies 2024, 1(1), 1-16; https://doi.org/10.3390/glacies1010001 - 29 Mar 2024
Viewed by 1462
Abstract
We conducted an experiment comparing wind speeds and aerodynamic roughness length (z0) values over three snow surface conditions, including a flat smooth surface, a wavy smooth surface, and a wavy surface with fresh snow added, using the wind simulation tunnel at [...] Read more.
We conducted an experiment comparing wind speeds and aerodynamic roughness length (z0) values over three snow surface conditions, including a flat smooth surface, a wavy smooth surface, and a wavy surface with fresh snow added, using the wind simulation tunnel at the Shinjo Cryospheric Laboratory in Shinjo, Japan. The results indicate that the measurement location impacts the computed z0 values up to a certain measurement height. When we created small (4 cm high) snow bedforms as waves with a 50 cm period, the computed z0 values varied by up to 35% based on the horizontal sampling location over the wave (furrow versus trough). These computed z0 values for the smooth snow waves were not significantly different than those for the smooth flat snow surface. Fresh snow was then blown over the snow waves. Here, for three of four horizontal sampling locations, the computed z0 values were significantly different over the fresh snow-covered waves as compared to those over the smooth snow waves. Since meteorological stations are usually established over flat land surfaces, a smooth snow surface texture may seem to be an appropriate assumption when calculating z0, but the snowpack surface can vary substantially in space and time. Therefore, the nature of the snow surface geometry should be considered variable when estimating a z0 value, especially for modeling purposes. Full article
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