Frequency Domain Multimodel Analysis of the Response of Atlantic Meridional Overturning Circulation to Surface Forcing
Bibliographic record
Abstract
Abstract The dynamics of the Atlantic meridional overturning circulation (AMOC) vary considerably among different climate models; for example, some models show clear peaks in their power spectra while others do not. To elucidate these model differences, transfer functions are used to estimate the frequency domain relationship between surface forcing fields, including sea surface temperature, salinity, and wind stress, and the resulting AMOC response. These are estimated from the outputs of the Coupled Model Intercomparison Project phase 5 (CMIP5) and phase 3 (CMIP3) control runs for eight different models, with a specific focus on Geophysical Fluid Dynamics Laboratory Climate Model, version 2.1 (GFDL CM2.1), and the Community Climate System Model, version 4 (CCSM4), which exhibit rather different spectral behavior. The transfer functions show very little agreement among models for any of the pairs of variables considered, suggesting the existence of systematic model errors and that considerable uncertainty in the simulation of AMOC in current climate models remains. However, a robust feature of the frequency domain analysis is that models with spectral peaks in their AMOC correspond to those in which AMOC variability is more strongly excited by high-latitude surface perturbations that have periods corresponding to the frequency of the spectral peaks. This explains why different models exhibit such different AMOC variability. These differences would not be evident without using a method that explicitly computes the frequency dependence rather than a priori assuming a particular functional form. Finally, transfer functions are used to evaluate two proposed physical mechanisms for model differences in AMOC variability: differences in Labrador Sea stratification and excitation by westward-propagating subsurface Rossby waves.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".