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Record W3156928146 · doi:10.1038/s43247-021-00140-8

The climate sensitivity of northern Greenland fjords is amplified through sea-ice damming

2021· article· en· W3156928146 on OpenAlexaff
Christian Stranne, Johan Nilsson, Adam Ulfsbo, Matt O’Regan, Helen K. Coxall, Lorenz Meire, Julia Muchowski, Larry A. Mayer, Volker Brüchert, Jonas Fredriksson, Brett F. Thornton, Julek Chawarski, Gabriel West, Elizabeth Weidner, Martin Jakobsson

Bibliographic record

VenueCommunications Earth & Environment · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsMemorial University of Newfoundland
FundersHorizon 2020 Framework ProgrammeVetenskapsrådetStockholms UniversitetNederlandse Organisatie voor Wetenschappelijk OnderzoekSvenska Forskningsrådet FormasEuropean CommissionPolarforskningssekretariatet
KeywordsFjordGreenland ice sheetGroenlandiaOceanographyArctic ice packArcticGeologySea iceIce sheetClimatologyPhysical geographyGeography

Abstract

fetched live from OpenAlex

Abstract Record-high air temperatures were observed over Greenland in the summer of 2019 and melting of the northern Greenland Ice Sheet was particularly extensive. Here we show, through direct measurements, that near surface ocean temperatures in Sherard Osborn Fjord, northern Greenland, reached 4 °C in August 2019, while in the neighboring Petermann Fjord, they never exceeded 0 °C. We show that this disparity in temperature between the two fjords occurred because thick multi-year sea ice at the entrance of Sherard Osborn Fjord trapped the surface waters inside the fjord, which led to the formation of a warm and fresh surface layer. These results suggest that the presence of multi-year sea ice increases the sensitivity of Greenland fjords abutting the Arctic Ocean to climate warming, with potential consequences for the long-term stability of the northern sector of the Greenland Ice Sheet.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.237
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2021
Admission routes1
Has abstractyes

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