Projected changes in temperature and precipitation indices in Morocco from high‐resolution regional climate models
Bibliographic record
Abstract
ABSTRACT The climate of Morocco is characterized by a strong spatial and inter‐annual variability. This study provides an evaluation of high‐resolution regional climate model ( RCM ) simulations of precipitation and temperature over Morocco and future projections based on two emission scenarios. The evaluation of the RCM ensemble over the historical period is performed with a network of 20 weather stations, using Taylor and Portrait diagrams. The results show that the four simulations considered ( CLM , CNRM , KNMI and IPSL ) are generally able to simulate climate indices and no model is performing significantly better. This ensemble of RCM simulations captures the precipitation and temperature spatiotemporal patterns in the evaluation and historical runs. Climate change scenarios are presented with the goal to identify spatial patterns of change over Morocco, to provide information for climate policy and adaptation. The RCP4 .5 and RCP8 .5 emission scenarios are considered for two time horizons, 2036–2065 and 2066–2095. A large increase in temperature is observed by the end of the century in particular for the RCP8 .5 scenario over the Southeast regions. The minimum temperature is expected to increase more than maximum temperature in most parts of Morocco, with the exception of the Eastern regions. The different RCMs show a strong agreement towards similar changes for most temperature‐based indices. The climate change signal is less homogeneous in the different simulations for most of the precipitation indices. Nevertheless, there is a clear decrease of precipitation totals in the different simulations, following a north to south gradient. However, for heavy rainfall events, there are strong uncertainties in projections and the four RCM simulations disagree about the future changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".