Calibration of Arctic ice core bromine enrichment records for past sea ice reconstructions
Bibliographic record
Abstract
Bromine in ice cores has been proposed as a qualitative sea ice proxy to produce sea ice reconstructions for the polar regions. Here we report the first statistical validation of this proxy with satellite sea ice observations by combining bromine enrichment (with respect to seawater, Br enr ) records from three Greenlandic ice cores (SIGMA-A, NU and RECAP) with satellite sea ice imagery, over three decades. We find that during the 1984–2016 satellite-era, ice core Br enr values are significantly correlated with first-year sea ice formed in the Baffin Bay and Labrador Sea supporting that the gas-phase bromine enrichment processes, preferentially occurring over the sea ice surface, are the main driver for the Br enr signal in ice cores. Moreover, in assessing Br enr 's capability to record historical sea ice variability, we compare 20th-century Arctic Sea ice historical and proxy records with our reconstructions, based on an autoregressive–moving-average (ARMA) model, finding overall good agreement. While further enhancements are warranted, including site-specific calibrations and a comprehensive investigation into bromine transport-related concerns, this study presents a new method to quantitatively reconstruct past seasonal sea ice variability through bromine enrichment in ice cores. • First statistical validation of bromine in ice cores as a sea ice proxy • Br enr values significantly correlate with first-year sea ice (FYSI) in Baffin Bay and Labrador Sea. • Ice core Br enr records overall captured historical Arctic sea ice variability. • Complex regions need complementary proxies for long-term sea ice reconstructions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".