A multi-model analysis of the decadal prediction skill for the North Atlantic ocean heat content
Bibliographic record
Abstract
Abstract. Decadal predictions can skilfully forecast upper-ocean temperatures in many regions worldwide. The North Atlantic, in particular, shows high predictive skill for the ocean heat content (OHC). This multi-model study analyses eight CMIP6 climate models with comparable decadal prediction (Decadal Climate Prediction Project, DCPP) and historical (HIST) ensembles to document differences in North Atlantic (NA) upper-OHC skill and investigates the underlying causes. The decadal predictions consistently identify two main regions with high predictive capacity and added value of initialization: the Labrador Sea (LS) and the eastern North Atlantic. A region east of the Grand Banks (EGB) is also found to exhibit negative skill scores, with its extent and location varying widely across models, possibly due in part to observational uncertainties affecting both forecast verification and local initialization. Special attention is given to the Labrador Sea and its surroundings, a region characterized by high inter-model spread in OHC prediction skill in both DCPP and HIST experiments. These differences hinder the identification of the relative contributions of external forcings and internal variability to local OHC predictability. To address this, we explore the relationship between the local OHC skill in the HIST ensemble and various mean-state properties in the Labrador Sea, revealing a strong link between the skill in those experiments and both the mean local surface fluxes and density stratification. Benchmarking these mean-state properties against observations and reanalyses suggests that the multi-model mean likely offers the most realistic estimate of the forced signal, accounting for approximately 16 % of the total OHC variance in the Labrador Sea. These findings underscore the critical role of stratification and atmospheric forcing biases in shaping predictive skill and highlight the potential of multi-model ensembles to advance our understanding of decadal predictability.
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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 | 0.000 |
| 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".