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Record W7071277362

Süßwasserschwankungen im arktisch-nordatlantischen Raum

2018· dissertation· en· W7071277362 on OpenAlexaboutno aff

Bibliographic record

VenueMedia (https://www.suub.uni-bremen.de/) · 2018
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsForcing (mathematics)Front (military)The arcticStaringArctic
DOInot available

Abstract

fetched live from OpenAlex

In the past decades, observations in the upper Arctic Ocean and subpolar North Atlantic have shown signicant freshwater changes that were in each region mainly attributed to independent processes. Both regions are sensitive to changes in the density stratication with possible implications for the ocean/atmsophere heat exchange and the deep convection. Thus changes in the freshwater content of the Arctic Ocean and subpolar North Atlantic have the potential to impact the climate locally and globally. The objectives of the present study are to investigate the freshwater content (co)variability of the upper Arctic Ocean and subpolar North Atlantic, to identify the processes causing the observed changes in freshwater content and to analyse possible drivers of these processes. To investigate the freshwater content variability I used objectively mapped salinity fields for the subpolar North Atlantic and Nordic Seas, and objectively mapped liquid freshwater inventories and sea ice volume estimates from the Pan-Arctic Ice Ocean Modeling and Assimilation product for the upper Arctic Ocean. To explore possible links, I compared the liquid freshwater content of the subarctic North Atlantic (SANA; combination of subpolar North Atlantic and Nordic Seas) with the sum of liquid and solid freshwater content of the upper Arctic Ocean from the observational and assimilation products. I found a distinct anti-correlation of the freshwater anomalies in these two regions between 1992 and 2013 with anomalies of the same magnitude. An analysis of freshwater fluxes from the global Finite Element Sea ice Ocean Model and the Common Ocean-ice Reference Experiment version 2 atmospheric forcing data set suggested that the observed freshwater variations resulted from changing freshwater transports. Variations in the Arctic freshwater export to the North Atlantic are found to be most important for the total freshwater content variability of the upper Arctic Ocean and for the liquid freshwater content variability of the western SANA. The eastern SANA freshwater content seems to be mainly influenced by the exchange with the subtropical North Atlantic. Furthermore, this study reveals that the observed freshwater changes are correlated with the Arctic and North Atlantic Oscillation indices. Therefore I suggest that a changing freshwater export from the Arctic Ocean to the SANA responds to decadal alternations of the dominant large-scale atmospheric variability. Thereby the export through the Canadian Arctic Archipelago is associated to different patterns of the atmospheric and oceanic pressure and circulation than the export through the Fram Strait and Barents Sea Opening. I propose, that the recently observed rapid changes in the SANA and upper Arctic Ocean freshwater content resulted from an interplay of these different driving patterns causing parallel changes in the freshwater export on both sides of Greenland. According to the present phase of the decadal alternations of the atmospheric variability and the final years in my freshwater content time series, the fresh water accumulated in the Arctic Ocean during the previous decades started to be released into the SANA. This release might continue in the following years and could have the potential to impact the Atlantic Meridional Overturning Circulation and the oceanic heat release to the Arctic atmosphere and sea ice.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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 designObservational
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
Published2018
Admission routes1
Has abstractyes

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