Mid-Holocene climate over the Mediterranean, North Africa, and Middle East simulated using a regional climate model
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".