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Record W4220830089 · doi:10.5194/egusphere-egu22-3522

Relative roles of different key forcing and preconditioning factors for recurrent deep convection in the Labrador Sea from observations and ocean models

2022· preprint· en· W4220830089 on OpenAlexaffabout
Patricia Handmann, Igor Yashayaev, Franziska U. Schwarzkopf

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsConvective mixingStratification (seeds)ConvectionForcing (mathematics)ClimatologyAdvectionOceanographyBuoyancyWater massDeep convectionThermohaline circulationOcean currentGeologyNorth Atlantic Deep WaterShutdown of thermohaline circulationAtmospheric sciencesEnvironmental scienceGeographyMeteorology

Abstract

fetched live from OpenAlex

The Labrador Sea in the subpolar North Atlantic is one of the few special regions, where strong wintertime buoyancy loss, consecutive substantially reduced vertical stratification, and the prevailing circulation facilitate the transfer of water mass properties from the surface to depths exceeding 1500 m through deep convective mixing. Hence, impacting the characteristics of the intermediate and deep waters in the entire Atlantic basin. Despite ever-growing evidence of the freshwater and atmospheric gas contents of these waters being directly affected by the strength of wintertime mixing in the Labrador Sea, the relative importance of the Labrador Sea convection for the strength of the overall Atlantic meridional overturning is still under debate, often leading to contradicting conclusions. This ongoing debate highlights the need for an in-depth all-inclusive investigation of the processes responsible for both occurrence and persistence of deep convective mixing events. Here, we make a first step in this direction by aligning multiplatform observations with model runs and quantifying the roles of the local atmospheric forcing (e.g., cumulative wintertime air-sea flux), the remote oceanic forcing (e.g., horizontal advection) and the ocean’s own memory of the past convective events (e.g., weak stratification resulting from convective preconditioning). These three key factors, fully responsible for initiation and undergoing of winter convection, and both seasonal and interannual heat content changes in the Labrador Sea, are analyzed based on long time series. These are comprised from all available thoroughly quality-controlled ship, profiling float and mooring measurements in the central Labrador Sea and state-of the-art ocean models. The resulting variables compared between the observations and models include time series of the characteristic ocean state variables, such as temperature, salinity and density over the entire water column. Additionally, the variables quantifying specific outcomes of each winter convection, such as depth, density and volume of the newly mixed intermediate-depth water in the Labrador Sea are considered. We show that the seasonal evolution of the deep winter convective mixed layer is a result of the sum of the surface cooling and the overall multiyear inertia in density changes and variations in the heat, freshwater and salt imports from the neighboring North Atlantic and Arctic regions. This, in turn means that not forcibly the strongest surface cooling induces the deepest convection with maximum density water, but rather a combination of the three factors. Through the combined analyses of observations and model-based time series we are able to properly assess the relative contribution of these three factors to the development of deep convective mixing in the Labrador Sea.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.226
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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