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Record W4416841474 · doi:10.1038/s41612-025-01277-5

Hysteresis response of Northern Hemisphere winter temperature variability under different CO₂ removal pathways

2025· article· en· W4416841474 on OpenAlexaboutno aff
Seung‐Ki Min, Soon-Il An, Maeng‐Ki Kim, Hyo‐Seok Park, Jong‐Yeon Park, Doo‐Sun R. Park, Hyun Min Sung, Young‐Hwa Byun, Kyung-On Boo

Bibliographic record

Venuenpj Climate and Atmospheric Science · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaKorea Meteorological AdministrationNational Research Foundation
KeywordsNorthern HemisphereHysteresisTemperature gradientZonal and meridionalArcticMagnitude (astronomy)The arcticThermal

Abstract

fetched live from OpenAlex

Arctic amplification weakens the meridional temperature gradient (∂T/∂y), reducing the Northern Hemisphere winter daily temperature variability (Tstd). However, the extent to which Tstd recovers following CO₂ removal remains uncertain. We investigate the hysteresis and reversibility of Tstd under various CO₂ pathways using UKESM1-0-LL. The mid-latitude Tstd reduced during the ramp-up phase partially recovers following CO₂ removal, but its magnitude depends on the region and peak CO₂ concentration. In the low-concentration experiments, Tstd nearly returns to pre-industrial levels, whereas the high-concentration experiments show hysteresis and irreversibility in eastern Canada and northwestern Eurasia. These regional differences are primarily driven by changes in local temperature gradients. Specifically, the dominant factor for eastern Canada is the meridional temperature gradient, whereas for northwestern Eurasia, it is the zonal temperature gradient related to the land-sea thermal contrast. These results suggest that the response of Tstd to CO 2 removal differs depending on the region and the peak concentration.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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 teacher head, 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
Published2025
Admission routes1
Has abstractyes

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