A climate modeling study over Northern Africa and the Mediterranean basin with multi-physics ensemble and coupling to a regional ocean modeling system
Bibliographic record
Abstract
We have developed a physics ensemble of Weather Research and Forecasting (WRF) model simulations for the Middle East, Mediterranean and North Africa (MEMNA) regions. These simulations use different configurations for the cumulus, microphysics, surface layer, planetary boundary layer, and land surface schemes and are forced by the Community Earth System Model (CESM) General Circulation Model for the historical period 1979–1993. We have also created a complementary ensemble in which the WRF model is fully-coupled to the Regional Ocean Modelling System (ROMS) that simulates the dynamics of the entire Mediterranean Sea. Analysis of our ensembles reveals that the simulated precipitation and near surface temperature (T2) fields in WRF are largely influenced by the cumulus and the land surface schemes during the summer and winter seasons, respectively. The coupling of Weather Research and Forecasting to Regional Ocean Modelling System yields Mediterranean sea surface temperatures that are directly correlated with T2 and have higher spatial resolution than the global model. Meanwhile no significant difference is found between the atmospheric fields from the coupled and uncoupled runs because the Community Earth System Model sea surface temperatures over the Mediterranean, that are used for surface forcing in the uncoupled runs, are already in close agreement with both Regional Ocean Modelling System and observations. We conclude that our high-resolution coupled atmosphere-ocean modelling system is capable of producing climate data of good quality, and we identify those combinations of physics schemes that result in an acceptable level of bias that facilitates their use in future studies.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".