Sensitivity of thermohaline circulation to decadal and multidecadal variability
Bibliographic record
Abstract
Abstract Lorenzo, M. N., Taboada, J. J., and Iglesias, I. 2009. Sensitivity of thermohaline circulation to decadal and multidecadal variability. – ICES Journal of Marine Science, 66: 1439–1447. In this paper, stochastic freshwater inputs with different variabilities are introduced into an Earth Model of Intermediate Complexity to study their effect on the behaviour of the thermohaline circulation (THC). The variability in the stochastic signal was set to be either decadal or multidecadal (70 years), based on intensity modulation of the El Niño-Southern Oscillation (ENSO) phenomenon. The results demonstrate a weakening of the THC in both the decadal and the multidecadal cases. This weakening results in a reduction in air surface temperature, mainly in the North Atlantic. Moreover, the 500-mb stream function also weakens. This causes lower rainfall in Western Europe, except in the areas most influenced by the Gulf Stream. Sea surface temperature is reduced significantly in the area around Greenland, whereas sea surface salinity is reduced around Greenland and in the Gulf Stream, but increased in the Labrador Sea and in Hudson Strait. The latter effects are more marked in the case where the variability of the inputs is multidecadal. The main implication of these results is that the natural decadal or multidecadal variability in freshwater inputs could have noticeable effects on the fate of the THC, which may be superimposed on the effects of climate change.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".