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Record W4411960395 · doi:10.5194/egusphere-2025-2957

The Green Sahara Re-desertification transition and its Climate Impacts over Northern Africa, Mediterranean Basin and the Levant

2025· preprint· en· W4411960395 on OpenAlexafffund
Fengyi Xie, Deepak Chandan, W. R. Peltier

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council
KeywordsDesertificationMediterranean climateGeographyMediterranean BasinStructural basinClimate changePhysical geographyGeologyArchaeologyOceanographyGeomorphologyEcology

Abstract

fetched live from OpenAlex

Abstract. This study presents an ensemble of atmosphere-ocean coupled Regional Climate Model (RCM) simulations for the Mid-Holocene (MH) climate across the Middle East, Mediterranean and North Africa (MENA) regions. This ensemble is generated using the Weather Research and Forecast (WRF) model with online coupling to the Regional Ocean Modelling System (ROMS) ocean model, which simulates the dynamics of the entire Mediterranean Sea, utilizing forcing derived from the Mid-Holocene (MH) climate generated from the UofT-CCSM4 General Circulation Model (GCM) with a prescribed Green Sahara (GS), and having a prescribed GS in the land surface in WRF, but allowing the land surface scheme in WRF to compute influence from GS. The results of this ensemble are characterized by an increased precipitation field, similar to previous results, and a 2 m surface temperature that is lower than those in results that have a fully prescribed GS in WRF. Further analysis determined that under the same GS prescription as in a previous study, the land surface scheme used in this study produces higher evaporation and a smaller change in albedo, jointly producing a lower 2 m surface temperature. This conclusion is supported by a new set of sensitivity experiments that modifies the prescribed land surface field, which also showed that the climate over the Middle East is sensitive to land surface states over northern Africa.

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 score0.851
Threshold uncertainty score0.995

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 studies0.0010.000
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 head0.037
GPT teacher head0.241
Teacher spread0.205 · 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 routes2
Has abstractyes

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