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Record W3043441011

Holocene Northern Hemisphere sea-ice distribution - proxy data reconstruction and modelling

2016· article· en· W3043441011 on OpenAlexaboutno aff
Marit‐Solveig Seidenkrantz, Anne de Vernal, Hugues Goosse, François Klein, Sandrine Solignac, Nicolas Van Nieuwenhove, Christof Pearce, Beth Caissie, Simon Belt, Longbin Sha, Ruediger Stein, Marc Macias‐Fauria, L. H. DeNinno

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsHoloceneProxy (statistics)Northern HemisphereClimatologyGeologyOceanographySea icePhysical geographyGeographyStatisticsMathematics
DOInot available

Abstract

fetched live from OpenAlex

A strikingly fast decrease of Arctic sea-ice cover has been recorded for the instrumental period and attributed to anthropogenic climate change, but little is known about natural sea-ice variability. Hence, there is a need for longer sea-ice time series to establish a baseline for natural Arctic sea-ice variability. We compiled 120 proxy-based sea-ice reconstructions from the Arctic Ocean and subarctic marginal seas to evaluate the stability/variability of sea-ice cover during the Holocene. The reconstructions are primarily based on published data combined with a few yet-unpublished records of biological (diatoms, dinoflagellate cysts, foraminifera, ostracods), sedimentological (IRD), and biogeochemical (IP25, PIP25, TOC) sea-ice indicators. Each indicator and record has been interpreted independently. We present all data as long-term annual means (months of sea ice per year). Sea-ice reconstructions are grouped into these classes: perennial (11-12 month/yr), dense (6-10 m/yr), common (1-6 m/yr), occasional (0.1-1 m/yr), rare (almost never) and absent (never). Further, reconstructions are made for the time slices 0-2 cal. ka (BP), 2-4 ka, 4-6 ka, 6±0.5 ka, 6-8 ka and 8-10 ka. Our study shows that winter sea ice was present during the entire Holocene, but summer sea ice may have been somewhat reduced in some areas during the Holocene Climate Optimum (10-6 ka), with variations between basins. In the Nordic Seas and N Atlantic minimum sea-ice conditions are seen 10-6 ka, whereas in the eastern Labrador Sea minimum sea-ice occurred 6-4 ka. Since ~4 ka sea-ice cover has increased, especially in the most recent millennia. Changes are subtle, however, but nonetheless consistent. The Pacific sector of the Arctic (Bering, Chukchi, Beaufort, Laptev, Okhotsk seas) shows less variability during the Holocene, though it is noted that these records have poorer age control and resolution than those from the Atlantic sector. It is noteworthy that, within the available temporal resolution, our data indicate that sea ice was present in the Arctic throughout the Holocene and that no longer periods of absence of sea ice occurred. Our proxy data interpretations have been used to constrain model output using data assimilation in the LOVECLIM model, focusing on the period 6±0.5 ka. This period of warmer than present summer conditions can help to understand the dynamics of the system in a warmer world. As expected, data assimilation leads to an overall better agreement with the reconstructions, mainly because of changes in the simulated wind patterns. Overall, the model simulation suggests that during the Holocene Thermal Maximum sea ice distribution was controlled by a strong positive Northern Annular Mode.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.213
Teacher spread0.194 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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