Surface density fluxes and water mass transformation over global oceans from reanalysis and climate models
Bibliographic record
Abstract
We analyze interannual and seasonal variability of surface density fluxes and water mass transformation rates over the global oceans for 1979-2018 using data from CFSR reanalysis and historical simulations by climate models. By analyzing density fluxes we quantify the the effect of surface heat and mass fluxes onto the formation of surface waters in the World Ocean. First, by using net fluxes from CFSR reanalysis we derive global climatology of surface density flux and further integrate it for density classes and T,S-classes, providing global and regional view of surface water mass transformation and its variability in space and in time. We precisely looked onto the role of salinity and sea ice formation in the density flux during the winter period. On average, the contribution of salinity to sea ice formation results in the differences of 9% in the density flux with the maximum effect of 12% identified in 1989. Interdecadal variability in surface transformation of the subpolar modal water and Labrador Sea waters shows opposite tendencies for the last decades. Then we analyze historical experiments from CMIP6 model ensemble and compare characteristics of surface water mass transformation with those revealed from reanalysis. We conclude that surface density fluxes and transformation rates derived from INM, MPI and MIROC are stronger compared to those diagnosed by CFSR with the largest differences identified over the Gulf Stream and the North Atlantic Current. For the same models we derive projections of surface density fluxes and surface water mass transformation for 2100 under ssp126, ssp370 and ssp585 scenarios. For all SSP scenarios, computations show a decrease in the magnitude of surface water mass transformation by the end of the century.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".