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Record W3096094299 · doi:10.5194/egusphere-egu21-14043

Ice stream formation

2021· preprint· en· W3096094299 on OpenAlexaff
Christian Schoof, Elisa Mantelli

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAdvectionGeologyIce divideDissipationSea ice growth processesMechanicsSTREAMSPressure ridgeGeophysicsSea iceDrift iceArctic ice packClimatologyPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Ice streams are the arteries through which a large fraction of the ice lost from Antarctica is discharged. With the introduction of "higher order" mechanics, the representation of ice streams in ice sheet models appears to have become more robust, eliminating previously ubiquitous grid effects. The detailed processes that control ice stream formation --- and the minimal ingredients that a model requires to represent them faithfully --- remain incompletely explored. Here we focus on "pure" ice streams, not confined to topographic troughs. We study two mechanisms that can cause their formation through feedbacks between enhanced dissipation and faster sliding, and study the minimal model capable of reproducing both mechanisms. In the first mechanism, increased dissipation raises basal temperature before the melting point is reached, and subtemperate sliding is in turn facilitated by these higher temperatures, leading to yet more dissipation. This mechanism has received very limited attention in the literature, and is not fully incorporated in at least some commonly used ice sheet model. The second, better-studied mechanism involves basal effective pressure rather than temperature as the degree of freedom that creates a positive feedback: increased dissipation produces additional meltwater. Draining that excess water requires a lower effective pressure in typical "distributed" draiange ssytems. Reduced effective pressure in turn leads to faster sliding, and yet more dissipation. The two mechanisms are distinct and one can operate in the absence of the other, but both can cause the formation of ice streams whose trunks have very similar features. Using a novel, hybrid `shallow/"full Stokes" flow' model derived from first principles, we show how accelerated flow due to either feedback leads to advection of cold ice to the bed, and demonstrate that this is the key negative feedback that controls ice steam formation due to its role in cooling the bed. Downward advection occurs both along the axis of the incipient ice stream, and in the transverse plane. There, a significant secondary flow towards the ice stream centre develops, which is of equal importance to along-flow advection in controlling heat transport. Our model is unique in its ability to fully resolve that secondary flow while still using the "shallowness" of the flow to simplify computations of ice stream physics. The formation of ice streams can be understood as "spatial" instabilities in which small-scale structure is amplified in the downflow direction, for which we derive an analytical criterion. Our model self-consistently predicts the formation of a sharply-defined ice stream margin and very cold-bedded ice ridges over a relatively short downstream distance from the onset of patterning for both mechanisms. The model also shows how basal dissipation in the margin leads to appreciable stream widening in the downstream direction, while englacial dissipation in combination with advection can lead to a pronounced peak in basal water supply some distance inside the margins. We demonstrate additionally that the emergent patterns can be unstable in time, and identify the properties required of a model that can handle such temporal instabilities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0130.002

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 head0.034
GPT teacher head0.226
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2021
Admission routes1
Has abstractyes

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