The Role of Ice–Ocean Interactions in the Variability of the North Atlantic Thermohaline Circulation
Bibliographic record
Abstract
The simulated influence of Arctic sea ice on the variability of the North Atlantic climate is discussed in the context of a global coupled ice–ocean–atmosphere model. This coupled system incorporates a general circulation ocean model, an atmospheric energy moisture balance model, and a dynamic–thermodynamic sea ice model. Under steady seasonal forcing, an equilibrium solution is obtained with very little variability. To induce variability in the model, daily varying stochastic anomalies are applied to the wind forcing of the Northern Hemisphere sea ice cover. These stochastic anomalies have observed spatial patterns but are random in time. Model simulations are run for 1000 yr from an equilibrium state and the variability in the system is analyzed. The sensitivity of the system to the ice–ocean coupling of both heat and freshwater is also examined. Under the stochastic forcing conditions, the thermohaline circulation (THC) responds with variability that is approximately 10% of the mean. This variability has enhanced spectral power at interdecadal timescales that is concentrated at approximately 20 yr. It is forced by fluctuations in the export of ice from the Arctic into the North Atlantic. Substantial changes in sea surface temperature and salinity are related to changes in the overturning circulation and the sea ice coverage in the northern North Atlantic. Additionally, the THC variability influences the Arctic Basin through heat transport under the ice pack. Results from sensitivity studies suggest that the freshwater exchange from the variable ice cover is the dominant process for forcing variability in the overturning. The simulated Arctic ice export appears to provide stochastic forcing to the northern North Atlantic that excites a damped oscillatory ocean-only mode. The insulating capacity of the variable sea ice has a negligible effect on the THC. Ice–ocean thermal coupling acts to damp THC variability, causing an approximately 25% reduction in the THC standard deviation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".