Future climate change projections in the agroecological, bioclimatic, biogeographical and altitudinal vegetation zones of Morocco
Bibliographic record
Abstract
Abstract Morocco is located in a region vulnerable to the impacts of climate change, which can have profound effects on its social, economic and environmental systems. This makes studies aimed at forecasting these impacts in future using climate models particularly important. However, the generally coarse spatial resolution of models, combined with a large number of models, imposes a limitation on the models, allowing the selection of the most appropriate ones for climate change impact assessments in a specific region. In this study, 38 GCMs and GCM‐RCMs from CMIP5 ensemble and CORDEX project were downscaled and bias‐corrected for use in projecting climate change over Morocco under the RCP4.5 and RCP8.5 scenarios. A three‐step sequential process was adopted, involving in that order the selection of models based on: (i) projection of climate means; (ii) projection of climate extremes; and (iii) ability of the models to simulate the baseline climate. Climate projections show precipitation decreases of up to 10% by the beginning of the century, with decreases of more than 20% under RCP8.5 projected by 2100, with the central and northern mountainous regions of the country being the most affected. Seasonal projections showed autumn months likely to experience the greatest decline in precipitation, up to 36.56% by the end of the century. Temperature projections revealed an upward trend in mean, maximum and minimum temperatures, with increases of up to 3°C predicted by mid‐century over most of the country, particularly in the winter months. Our results point to a concerning future, with impacts related to decreased precipitation and increased temperatures expected to be many and varied across the country. Nevertheless, they can help provide a knowledge base for efforts to mitigate and adapt to expected changes.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".