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Record W3209617986 · doi:10.5281/zenodo.3361567

Assessment of Oceanic Anomalies of Predictive Potential (D2.5)

2019· article· en· W3209617986 on OpenAlexaboutno aff
Tor Eldevik, Marius Årthun

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
FundersEuropean Commission
KeywordsGeologyClimatology

Abstract

fetched live from OpenAlex

Climate prediction is the challenge to forecast climatic conditions months to decades into the future with a skill and regional detail that is of practical use. Will, for example, Arctic sea ice cover increase the next winter? Will Scandinavian hydroclimate be particularly beneficial for hydropower production? Will Southern European summers be excessively warm through the 2020s? To what extent such conditions are predictable in nature and to what extent predictability can be realised in operational climate forecast systems and translated to useful stakeholder information, i.e., the climate equivalent to weather forecasting, remain unknown. It is commonly understood that predictability resides with the more inert components of the climate system and particularly—as is the focus of Blue-Action—with ocean circulation. Blue-Action has substantiated this premise by exploring observations, climate models, and reanalyses (model simulations tightly constrained by available observations). Successful avenues of research and progress made in Blue-Action include mapping out the dominant timescales of European interannual-to-decadal climate variability, the identification of consistent and predictable variability in Atlantic-to-Arctic ocean circulation, the link of ocean variability to fluctuating climate over land and sea ice extent, and making actual climate forecasts toward 2020. We find that winter surface air temperatures for much of continental Europe, and generally for Eastern Europe, are characterized by a 5–10 year timescale. This reflects the variable strength of the large-scale westerly winds, commonly summarized in the so-called North Atlantic Oscillation (NAO) index. The longest timescale that our observation-based record resolves is a 25–40 year fluctuation characteristic for Western Europe (Iberia and England). This we associate with more hemisphere-scale coherent temperature fluctuations, the so-called Atlantic Multidecadal Oscillation (AMO). The winter temperature over Northwest Europe (Norway, Sweden and parts of Finland), on the other hand, is dominated by an intermediate 14-year timescale coherent with the sea surface temperature of the Norwegian Sea. Fluctuating warm and cold ocean states off the east coast of Newfoundland progress persistently with ocean circulation to and through the Norwegian Sea subsequently to face the Arctic sea ice cover. Our analyses show that this variable ocean heat arrives predictably in the Norwegian Sea and imprints on the surface air temperature carried in over Scandinavia “downwind” with the mean westerly wind with a forecast horizon of 7–10 years. We particularly predict (from data through to 2016) that Norwegian mean temperature and precipitation will decrease, although very marginally so (and remain above 1981–2010 climatology), toward 2020. And a general mean temperature decrease was indeed observed from 2014 through to 2018 (but only slightly from 2016). Similarly, winter sea ice extent in the Atlantic sector is predicted slightly to increase (but remain below climatology). This is qualitatively reflected in the fact that Arctic March winter maximum increased – also relatively slightly – from 2017 to 2018, and then again from 2018 to 2019. A basis for exploring climate predictability is the access to, and maintenance of, key observational time series. Blue-Action benefits from the collective effort of the scientific community in making repeated observations of the ocean and from satellite remote sensing of the sea surface. Importantly, Blue-Action contributes observations to this effort with full-depth hydrographic and current meter measurements of the water mass exchanges at the Greenland-Scotland Ridge, the main gateway between the North Atlantic and the Arctic region—including the passage into the Norwegian Sea of the Gulf Stream’s northern limb and the cold and warm ocean states giving rise to climate predictability as alluded to above. A priority for Blue-Action is therefore both the maintenance and consistency of these main time series of northern ocean climate, and further to deepen the understanding of the full spectrum of variability reflected at the mooring sites (predictable and otherwise). We find that at seasonal time scales, the vigour of the exchanges generally reflects that of the regional wind (broadly speaking the NAO), but that changes in so-called buoyancy forcing (heat loss and freshwater input) seems increasingly important with increasing timescales. The current meter measurement records, initiated by several of the Blue-Action partners through the 1990s, are still too short for confidently concluding on the nature of observed variability at timescales longer than the interannual. Further progress in “taking the pulse” of the Gulf Stream’s interaction with the Arctic is therefore from an observational perspective critically dependent on the future maintenance and thus increasing temporal range covered by the Greenland-Scotland Ridge observational array.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.249
Teacher spread0.233 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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