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Gradual and Abrupt Permafrost Thaw as Drivers of Rapid Geomorphic Change in Arctic Permafrost Regions

2020· article· en· W3205182170 on OpenAlexaffabout
Guido Grosse, Julia Boike, Louise Farquharson, Benjamin Jones, Moritz Langer, Hugues Lantuit, Anna Liljedahl, Ingmar Nitze, Alexandra Runge, V. E. Romanovsky, Thomas Schneider von Deimling, Warwick F. Vincent, Donald A. Walker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalCenter for Northern Studies
Fundersnot available
KeywordsPermafrostArcticClimate changeThermokarstEnvironmental sciencePhysical geographyCryosphereGlobal warmingSea iceGeologyHydrology (agriculture)ClimatologyOceanographyGeography

Abstract

fetched live from OpenAlex

In this presentation, we will highlight some of the overarching geomorphic dynamics of gradual and abrupt permafrost thaw using examples from Siberia, Alaska, and Canada, their role in Arctic landscapes transitioning to a warmer world, and implications for the Earth System. Northern high latitude regions are particularly vulnerable to warming and changes in climatic patterns, which leads to the thaw of ice-rich permafrost across vast Arctic and sub-Arctic landscapes. Permafrost thaw and subsequent geomorphological change directly interact with hydrology, biogeochemistry, and biology and thus have been major agents of Arctic ecosystem change since the last deglaciation. The changes are also important contributors to cumulative impacts associated with historic and current development of the Arctic, including in association with infrastructure. Today, in a rapidly warming Arctic, permafrost thaw processes, both gradual and abrupt, are accelerating in a manner comparable to the Holocene Thermal Maximum. At the same time, other environmental forcing factors, such as wildfires, precipitation, and hydrological processes, are also changing, either further reinforcing thaw dynamics or enhancing drainage and stabilizing the ground. Many of the resulting gradual and abrupt thaw processes are non-linear. Their dynamics are still poorly understood and insufficiently quantified in large-scale models due to lacking or limited representation of water-ice phase transitions during freeze-thaw cycles, ground ice distribution and loss, and challenging implementation of sub-gridcell scale interactions between frozen ground and hydrology. Gradual thaw impacts are especially pronounced in regions with an abundance of ice wedge polygons, and include changes in microtopography and extensive ponding in natural landscapes and those adjacent to infrastructure, where soils are warmed due to increased dust, flooding, snowdrifts, and altered vegetation. Abrupt thaw processes such as thermokarst and thermo-erosion represent rapid dynamics that are widespread in Arctic lowlands but poorly represented in observations and models. Characteristic landforms that result from abrupt thaw include thermokarst lakes and basins, retrogressive thaw slumps, and steep coastal bluffs. These landscape changes may be triggered by climate-driven press disturbances such as sea ice loss or increases in precipitation, pulse disturbances such as wildfires, or by anthropogenic disturbances such as road construction. When loss of excess ground ice is involved, positive feedbacks can result in a decoupling of further geomorphological change from climate. Once initiated, this may lead to continued or even accelerated growth of such features under a wide range of climate conditions, including in the high Arctic. Most abrupt thaw processes produce lasting impacts on northern permafrost landscapes that are irreversible over millennial timescales and result in the short-term mobilization of large amounts of permafrost carbon that may further contribute to climate warming.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.079
GPT teacher head0.253
Teacher spread0.173 · 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 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

Citations0
Published2020
Admission routes2
Has abstractyes

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