This ethnographic study at the US National Snow and Ice Data Center (NSIDC) follows a group of scientists and communications specialists as they compose visualizations and analyses of near-real-time Arctic sea ice data. Research participants collectively make scientific judgments about near-real-time data in a highly visible public venue with 'relational agility'. They balance multiple phenomena including knowledge of how sceptics attack climate science, reflexivity about the conventions through which sea ice data is gathered, the needs of journalists working in a news cycle paced by Twitter, and the liveliness and vitality of sea ice itself. Relational agility, understood as a way of coordinating the social in relation to this plurality of contingent practices and processes, provides insight into the science and politics of nonlinear climate change.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1177/0306312720906532 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Impact of Glaciers on Trophic Dynamics and Polyunsaturated Fat Accumulation in Southern Greenland Fjord Ecosystems.
Glob Chang Biol
January 2025
Department of Surface Waters-Research and Management, EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland.
The primary production of fjords across the Arctic and Subarctic is undergoing significant transformations due to the climatically driven retreat of glaciers and ice sheets. However, the implications of these changes for upper trophic levels remain largely unknown. In this study, we employ both bulk and compound-specific stable isotope analyses to investigate how shifts at the base of fjord food webs impact the carbon and energy sources of consumers.
View Article and Find Full Text PDFRevisiting the Last Ice Area projections from a high-resolution Global Earth System Model.
Commun Earth Environ
January 2025
Recherche en Prévision Numérique Environnementale/Environnement et Changement Climatique Canada, Dorval, QC Canada.
The Last Ice Area-located to the north of Greenland and the northern Canadian Arctic Archipelago-is expected to persist as the central Arctic Ocean becomes seasonally ice-free within a few decades. Projections of the Last Ice Area, however, have come from relatively low resolution Global Climate Models that do not resolve sea ice export through the waterways of the Canadian Arctic Archipelago and Nares Strait. Here we revisit Last Ice Area projections using high-resolution numerical simulations from the Community Earth System Model, which resolves these narrow waterways.
View Article and Find Full Text PDFCollection of optimizations for untargeted metabolomics analysis of Leishmania promastigotes using gas chromatography-mass spectrometry.
Talanta
January 2025
Center for Multiplatform Metabolomics Studies (CEMM) at the Institute of Chemistry, University of Sao Paulo, Sao Paulo, SP, 05508-000, Brazil. Electronic address:
There is no consensus in the literature regarding the ideal protocol for obtaining and preparing cell samples for untargeted metabolomics. Nevertheless, the procedures must be carefully evaluated for proper and reliable results for each organism under study. This work proposes a novel protocol for determining intracellular metabolites in Leishmania promastigotes and is fully optimized for application in conjunction with gas chromatography-mass spectrometry platforms.
View Article and Find Full Text PDFTowards the Measurement of Sea-Ice Thickness Using a Time-Domain Inductive Measurement System.
Sensors (Basel)
January 2025
Department of Electronic and Electrical Engineering, University of Manchester, Manchester M13 9PL, UK.
Frequency-domain electromagnetic induction (EMI) is routinely used to detect the presence of seawater due to the inherent electrical conductivity of the seawater. This approach is used to infer sea-ice thickness (SIT). A time-domain EMI sensor is presented, which demonstrates the potential for correlating the spectroscopic properties of the received signal with the distance to the sea surface.
View Article and Find Full Text PDFPerformance Evaluation of Deep Learning Image Classification Modules in the MUN-ABSAI Ice Risk Management Architecture.
Sensors (Basel)
January 2025
Faculty of Engineering and Applied Science, Memorial University of Newfoundland (MUN), St. John's, NL A1B 3X5, Canada.
The retreat of Arctic sea ice has opened new maritime routes, offering faster shipping opportunities; however, these routes present significant navigational challenges due to the harsh ice conditions. To address these challenges, this paper proposes a deep learning-based Arctic ice risk management architecture with multiple modules, including ice classification, risk assessment, ice floe tracking, and ice load calculations. A comprehensive dataset of 15,000 ice images was created using public sources and contributions from the Canadian Coast Guard, and it was used to support the development and evaluation of the system.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!