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Record W4387460896 · doi:10.1080/07055900.2023.2264832

Assessment of Ocean Temperature Trends for the Scotian Shelf and Gulf of Maine Using 22 CMIP6 Earth System Models

2023· article· en· W4387460896 on OpenAlexaffvenue
Zeliang Wang, David Brickman, B. J. W. Greenan, James R. Christian, Brendan DeTracey, Denis Gilbert

Bibliographic record

VenueATMOSPHERE-OCEAN · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersCollaborative Spine Research Foundation
KeywordsSea surface temperatureClimatologyCoupled model intercomparison projectOceanographyEnvironmental scienceClimate systemPeriod (music)Climate modelClimate changeGeology

Abstract

fetched live from OpenAlex

This study examined the results of 22 CMIP6 (Coupled Model Inter-comparison Project phase 6) Earth System Model (ESM) simulations for four regions on the Scotian Shelf and Gulf of Maine. A comparison between the historical simulations from the CMIP6 ESMs with observational sea surface and bottom temperature (SST, BT) data demonstrates that the eddy-permitting ESMs do not perform better than coarse-resolution non-eddy permitting models in terms of long-term trends. Eddy-permitting ESMs reduce model SST bias but not BT bias. In general, the 22 CMIP6 ESMs show limited skill for historical BT simulations in these shelf regions. Climate projections under ssp (Shared Socio-economic Pathways)245 and ssp370 for the 2020–2049 period suggest that the largest seasonal SST increase will occur in summer for both the Scotian Shelf and the Gulf of Maine. Under both climate scenarios, the SST of the Scotian Shelf (Gulf of Maine) increases by 1.2–1.8 °C (1.4–1.7 °C) for the 2040–2049 period relative to 1995–2014, and bottom temperature increases by 1.2–1.6°C (1.3–1.4 °C) for the same period. For SST, five models exhibit abnormally warm projections. The ESMs’ performance against observations suggest the SST changes are probably underestimated, while the BT changes are likely overestimated.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.260
Teacher spread0.239 · 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.

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

Citations6
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueATMOSPHERE-OCEANSame topicClimate variability and modelsFrench-language works237,207