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Record W4408429636 · doi:10.5194/egusphere-egu25-15866

A novel method for sea ice reconstructions using satellite calibration of bromine enrichment records in Arctic ice cores

2025· preprint· en· W4408429636 on OpenAlexaboutno aff
Federico Scoto, Niccolò Maffezzoli, Alfonso Saiz‐Lopez, Carlos A. Cuevas, Alessandro Gagliardi, Cristiano Varin, Andrea Spolaor

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSea iceSatelliteArctic ice packBromineCalibrationArcticIce coreRemote sensingThe arcticGeologyOceanographyChemistryPhysicsAstronomy

Abstract

fetched live from OpenAlex

Paleo-records such as marine sediments and ice cores are commonly used to extend our knowledge about past sea-ice cover during the period prior to instrumental observations. Several studies (Spolaor et al., 2016, Saiz Lopez and Von Glasow, 2012) have identified bromine in ice cores as a potential proxy for past sea ice conditions. During polar springtime, in fact, the photochemical recycling of bromine is extremely efficient over first year sea ice (FYSI), resulting in enhanced concentrations of inorganic gas phase bromine (e.g. BrO) compared to the ocean surface, multi-year sea ice or snow-covered land. This process is known as “bromine explosion” and is detected by satellite sensors and in-situ observations from early Marchto late May. After emission, the BrO plume is frequently carried for several days by high-latitude cyclones in the lower troposphere until it reaches land and falls in the form of bromine enriched snow compared to seawater Br/Na ratio. Here, we present the first statistical validation of this proxy using satellite sea ice observations. By combining bromine enrichment (relative to seawater, Brenr) records from three Greenlandic ice cores with satellite sea ice imagery over a span of three decades, we demonstrate its efficacy. During the satellite era (1984–2016), Brenr values in the ice cores show significant correlations with first-year sea ice formed in the Baffin Bay and Labrador Sea, confirming that gas-phase bromine enrichment processes, which predominantly occur over sea ice surfaces, are the primary drivers of the Brenr signal in ice cores. Furthermore, to evaluate Brenr’s ability to capture historical sea ice variability, we compare 20th-century Arctic sea ice historical records and proxy data with reconstructions derived from an autoregressive–moving-average (ARMA) model. The results show overall strong agreement. While further improvements are needed—such as site-specific calibrations and detailed studies on bromine transport dynamics—this study introduces a novel quantitative method for reconstructing past seasonal sea ice variability using bromine enrichment in ice cores

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.036
GPT teacher head0.292
Teacher spread0.256 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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