The impact of ocean tides on a climate model simulation.
Bibliographic record
Abstract
We explicitly include the forcing of ocean tides in a global ocean general circulation model (OGCM). The tidal forcing is deduced from lunisolar ephemerides according to the instantaneous positions of moon and sun. In this real-time approach we consider the complete lunisolar tides of second degree. The OGCM is part of a state-of-the-art climate model which was used for the fourth assessment report simulations of the Intergovernmental Panel on Climate Change (IPCC). An ensemble of five IPCC A1B climate scenarios covering the period 1860 to 2059 has been computed. The induced tidal currents affect the ocean circulation by nonlinear interaction and through vertical mixing. The latter is described in the model by a Richardson number dependent mixing term. Thus, mixing depends on the density stratification and the vertical velocity shear. In regions of high tidal velocities the vertical velocity shear is enhanced in the deepest layers induced by bottom friction. Our study focuses on the North Atlantic region, where the highest tidal velocities occur. There, the representation of the present state of the ocean is improved significantly. The tides adjust the pathway of the North Atlantic Current, which leads to improved sea surface temperatures of up to 3 degree in the North Atlantic. Further, the simulation of the deep convection in the Labrador Sea, one of the driving mechanisms of the meridional overturning circulation, becomes more realistic when forcing ocean tides. The modified oceanic dynamics in the North Atlantic have implications for the simulation of the European climate and for the future projection of the sea surface temperature of the North Atlantic. This study reveals that ocean tides are an important component in the simulation of ocean dynamics and are essential for an appropriate simulation of a changing ocean under climate warming conditions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".