A multi-model analysis of the decadal prediction skill for the North Atlantic ocean heat content
Bibliographic record
Abstract
Abstract. Decadal predictions can skillfully forecast the upper ocean temperature in many regions of world. The North Atlantic in particular shows promising results when it comes to high predictive skill of Ocean Heat Content (OHC). Nevertheless, important regional differences exist across Decadal Prediction Systems, which are explored in this multi-model analysis. Differences are also found in their respective uninitialized historical ensembles, which points to large uncertainties in the externally forced signals. We analyze eight CMIP6 climate models with comparable ensembles of decadal predictions and historical simulations to document their differences in upper OHC skill, and to investigate if intrinsic model characteristics, such as key mean state biases in the local forcing from the atmosphere or the local stratification, can influence the relative predictive role of external forcings and internal variability. Particular attention has been given to the Labrador Sea and its surroundings, since this is found to be a region where upper OHC has low observational uncertainties, yet high inter-model spread in the upper OHC prediction skill of decadal predictions and historical experiments. Benchmarking mean state properties of the local surface fluxes and stratification against observations, both strongly linked with the simulated upper OHC skill for the historical ensembles, suggests that their multi-model mean provides the most realistic estimate of the true forced signal
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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 | 0.002 |
| 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 | 0.001 |
| 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".