Low-frequency variability of the arctic climate: the role of oceanic and atmospheric heat transport variations
Bibliographic record
Abstract
Changes in meridional heat transports, carried either by the atmosphere (HTRA) or by the ocean (HTRO), have been proposed to explain the decadal to multidecadal climate variations in the Arctic. On the other hand, model simulations indicate that, at high northern latitudes, variations in HTRA and HTRO are strongly coupled and may even compensate each other. A multi-century control integration with the Max Planck Institute global atmosphere-ocean model is analyzed to investigate the relative role of the HTRO and HTRA variations in shaping the Arctic climate and the consequences of their possible compensation. In the simulation, ocean heat transport anomalies modulate sea ice cover and surface heat fluxes mainly in the Barents Sea/Kara Sea region and the atmosphere responds with a modified pressure field. In response to positive HTRO anomalies there are negative HTRA anomalies associated with an export of relatively warm air southward to Western Siberia and a reduced inflow of heat over Alaska and northern Canada. While the compensation mechanism is prominent in this model, its dominating role is not constant over long time scales. The presence or absence of the compensation is determined mainly by the atmospheric circulation in the Pacific sector of the Arctic where the two leading large-scale atmospheric circulation patterns determine the lateral fluxes with varying contributions. The degree of compensation also determines the heat available to modulate the large-scale Arctic climate. The combined effect of atmospheric and oceanic contributions has to be considered to explain decadal-scale warming or cooling trends.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".