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Record W1029104004

North Atlantic multidecadal to centennial variability in a model and a marine proxy dataset

2013· dissertation· en· W1029104004 on OpenAlexfundno aff
Jennifer Mecking

Bibliographic record

VenueHelmholtz Centre for Ocean Research Kiel (GEOMAR) · 2013
Typedissertation
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche ForschungsgemeinschaftAlexander von Humboldt-Stiftung
KeywordsClimatologyAtlantic multidecadal oscillationPredictabilitySea surface temperatureProxy (statistics)North Atlantic oscillationClimate modelPacific decadal oscillationEnvironmental scienceOceanographyLatitudeGeographyClimate changeGeology
DOInot available

Abstract

fetched live from OpenAlex

Variability on decadal and longer timescales is of great interest in climate research due to it’s socio-economic impacts, potential for predictability and masking of anthroprogenic global warming. Observational evidence of multidecadal variability in the North Atlantic exists in the sea surface temperature (SST), often referred to as the Atlantic Multidecadal Variability (AMV), and also in the atmosphere, for example seen in sea level pressure variations associated with the North Atlantic Os- cillation (NAO). Observational oceanic data on these timescales is mainly restricted to the surface, does not extend past the last 145 years and becomes quite sparsely sampled in the higher latitudes in the earlier years. Hence, to increase our understanding of climate variability on these timescales it is essential to turn to both proxy and model data. The first part of this thesis focuses on an annually-resolved proxy record (1818- 1967) of Mg/Ca variations from a North Pacific/ Bering Sea coralline alga. Not only does the algal Mg/Ca have a very strong connection to the local winter SST and a lagged relation to the Aleutian Low it also it has a correlation of -0.87 with the winter NAO and 0.60 with the AMV index on decadal timescales. The link can explain the coherence of decadal North Pacific and AMV, as suggested by earlier studies using climate models and in the limited observational data. The second part of the thesis focuses on the ocean general circulation model, NEMO to better understand AMV. For this purpose the model was forced only with the atmospheric patterns associated with the NAO, both from the observed NAO index and from a 2000 year long white NAO index. Focusing on key ocean circulation patterns, we show that the Atlantic Meridional Overturning Circula- tion (AMOC) and sub-polar gyre (SPG) strength have a red noise response but no dominant timescale, providing no evidence for a oscillatory ocean-only mode of variability. The time derivative of both the AMOC at 30◦N and SPG strength show a strong, almost linear relation to the NAO for timescales longer than 86 and 15 years, respectively. The different response characteristics are confirmed by constructing simple statistical models that show AMOC and SPG variability can be reconstructed by integrating the NAO index by the previous 53 and 10 winters, respectively. Alternatively, the AMOC and the SPG strength can be reconstructed with auto-regressive (AR) models of order seven and five, respectively. A closer look at the ocean model response of the 2000 year long ocean model integration shows three distinct timescales of variability. The first, an interannual timescale with variability shorter than 15 years, can be mainly related to Ekman dynamics. Secondly, the multidecadal timescale, 15-65 years, is mainly concentrated in the SPG and is controlled by temperature variability. Finally, the centennial timescales, with variability longer than 65 years, can be attributed to the ocean being in a series of quasi-equilibrium with the forcing. The statistical models presented in this thesis to reconstruct the AMOC and SPG strength on multidecadal and longer timescales can be useful for prediction and model inter-comparision.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.037
GPT teacher head0.324
Teacher spread0.287 · 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 teacher head, not a consensus.

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
Published2013
Admission routes1
Has abstractyes

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