An ensemble reconstruction of ocean temperature, salinity, and the Atlantic Meridional Overturning Circulation 1960–202
Bibliographic record
Abstract
Ocean reanalyses covering many decades, including those with few observations, are needed to understand climate variability and to initialize and assess interannual to decadal climate predictions. The Met Office Statistical Ocean Re-Analysis (MOSORA) exploits long-range covariances to generate full-depth reanalyses of monthly ocean temperature and salinity even from sparse observations. The latest version of MOSORA presented here is for the first time an ensemble that samples uncertainties in these long-range covariances. The ensemble is created by using initial covariances from different perturbed-physics historical model runs and these are then improved with observations using an iterative process.We demonstrate that covariances are mostly improved by iteration, and that this procedure, using very sparse observations typical of the 1960s, captures many features of analyses benefiting from modern observation density. We investigate the ensemble spread and find that salinity trends in the covariances from model runs can introduce unexpected changes in the reanalyses. In the Gulf of Guinea, there are insufficient observations to constrain the model covariances, which vary due to different model representations of Antarctic Intermediate Water. If models are improved in this region, this could lead to a better analysis of temperature and salinity.We nudge the reanalyses into an ensemble of coupled climate models to produce estimates of the Atlantic Meridional Overturning Circulation (AMOC) back to 1960. At 26°N, the AMOC shows decadal variability consistent with observations at this latitude and shows signs of strengthening in recent years. The ensemble spread in AMOC reconstructions at this latitude increases with time as more observations interact with uncertain covariances. More observations should be able to better constrain these covariances.At 45°N, the amount of decadal variability in the AMOC varies between members. The uncertainty of our reconstruction at this latitude varies through time partly related to the number of observations made on the western boundary, just off the Grand Banks of Newfoundland. This shows potential for targeted and sustained observations to constrain the transport into the North Atlantic subpolar gyre.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".