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Record W4412140342 · doi:10.1177/09596836251350243

Mid-Holocene climate over the Mediterranean, North Africa, and Middle East simulated using a regional climate model

2025· article· en· W4412140342 on OpenAlexaff
Fengyi Xie, Deepak Chandan, W. R. Peltier

Bibliographic record

VenueThe Holocene · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHoloceneMediterranean climateClimate changeClimate modelMiddle EastClimatologyGeologyPhysical geographyPaleoclimatologyOceanographyGeographyArchaeology

Abstract

fetched live from OpenAlex

We present an ensemble of atmosphere-ocean coupled Regional Climate Model (RCM) simulations for the Middle East, Mediterranean and North Africa regions. These simulations are forced by Mid-Holocene (MH) climate generated using the UofT version of NCAR CCSM4 global climate Model (GCM), with or without the prescription of Green Sahara (GS) boundary conditions. Our ensemble members consist of atmosphere-ocean coupled simulations in which the Weather Research and Forecast (WRF) model is fully-coupled to the Regional Ocean Modeling System (ROMS) that simulates the dynamics of the entire Mediterranean Sea. When forced using data from a GCM simulation that does not include GS boundary conditions, the RCM simulates a MH that is similar to the GCM at large scales, but with increased spatial detail. When forced using a GCM simulation that includes GS boundary conditions, the RCM responds with an increase in monsoon precipitation and an amplified seasonal cycle. If, on the other hand, both the GCM and the RCM include GS boundary conditions, then RCM response is characterized by a further increase in monsoon precipitation and a further modification of the annual cycle in temperature. Results from the latter version of the downscaling pipeline are shown to provide a much improved reconsiliation of proxy climate records. This result establishes the validity of our downscaling pipeline for the simulation of MH climate over the target region of interest.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.816

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.096
GPT teacher head0.276
Teacher spread0.180 · 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 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
Published2025
Admission routes1
Has abstractyes

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