The freezing‒thawing index and permafrost extent in pan-Arctic experienced rapid changes following the global warming hiatus
Bibliographic record
Abstract
Global ground surface temperatures experienced a rapid increase following the end of the global warming hiatus in 2013. The rapid temperature increase has potential to drive changes in FDD/TDD and permafrost extent in pan-Arctic. In this study, the temporal and spatial trends of air freezing‒thawing index (AFDD/ATDD) and ground surface freezing‒thawing index (GFDD/GTDD) in pan-Arctic from 2003 to 2023 were analyzed, with a particular focus on the changes between the decades preceding and following 2013. The changes in permafrost extent were also analyzed. The results indicate that from 2003 to 2023, the AFDD and GFDD significantly ( p < 0.05) decreased at rates of 11.07 and 5.34 °C d per year, while the ATDD and GTDD significantly increased at rates of 7.69 and 4.34 °C d per year ( p < 0.05), respectively. The FDD/TDD experienced rapid changes after the global warming hiatus, with the decreasing rates in AFDD and GFDD intensifying after 2013 to 15.75 °C d per year and 6.27 °C d per year, respectively, and increasing rates in ATDD and GTDD intensifying after 2013 to 15.88 °C d per year and 8.50 °C d per year, respectively. The permafrost area in pan-Arctic experienced a decline from 13.4 × 10 6 km 2 in 2003–2013 to 12.51 × 10 6 km 2 in 2014–2023, representing a decadal reduction of 0.78 × 10 6 km 2 (6.64%). The rapid decadal reduction in permafrost extent surpassed the decadal changes projected by historical trend since 1901, despite a slight expansion in southern Canada. The results provide novel insights into recent changes in FDD/TDD and permafrost extent.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".