The observation-based application of a Regional Thermohaline Inverse Method to diagnose the formation and transformation of Labrador Sea water from 2013-2015
Bibliographic record
Abstract
The strength of the Meridional Overturning Circulation (MOC) in the North Atlantic is dependent upon the formation of dense waters that occurs at high northern latitudes. Wintertime deep convection in the Labrador and Irminger Seas forms the intermediate water mass known as Labrador Sea Water (LSW). Changes in the rate of formation and subsequent export of LSW are thought to play a role in MOC variability, but formation rates are uncertain and the link between formation and export is complex. We present the first observation-based application of a recently developed Regional Thermohaline Inverse Method (RTHIM) to a region encompassing the Arctic and North Atlantic subpolar gyre for the years 2013, 2014 and 2015. RTHIM is a novel method which can diagnose the formation and export rates of water masses such as the LSW identified by their temperature and salinity, apportioning the formation rates into contributions from surface fluxes and interior mixing. We find LSW formation rates of up to 12 Sv during 2014-15, a period of strong wintertime convection, and around half that value during 2013 when convection was weak. We also show that the newly convected water is not exported directly, but instead is mixed isopycnally with warm, salty waters that have been advected into the region, before the products are then exported. RTHIM solutions for 2015 volume, heat and freshwater transports are compared with observations from a mooring array deployed for the Overturning in the Subpolar North Atlantic Program and show good agreement, lending validity to our results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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".