Methodological Approach for Climate Simulations Selection for Climate Change Impact Studies
Bibliographic record
Abstract
Abstract This study builds on previous studies and proposes a combined approach for shortlisting and selecting representative climate simulations for impact studies in the Souss watershed in Morocco. The selection procedure consists of selecting climate simulations on the basis of predicted changes in means and extremes, climate model performance in simulating past climate and bias correction. Unlike the selection methods proposed in the literature that begin with a pre-selection of simulations based on changes in means, the approach recommended in this study involves an initial pre-selection of simulations by the ability of the models to simulate the base period climate. The proposed procedure involved selecting a set of general circulation models (GCMs) from the Phase 5 Coupled Model Intercomparison Project (CMIP5) and regional climate models (RCMs) from the Coordinated Regional Downscaling Experiment (CORDEX) project to project spatiotemporal changes in precipitation and temperature for the RCP4.5 and RCP8.5 scenarios. Climate changes between 1975–2005 and 2066–2095 were analyzed for the watershed. By 2066–2095, precipitation is projected to decrease by -12.2 to -30.1% under the RCP4.5 scenario and by -5.2 to -65.4% under the RCP8.5 scenario. Mean temperature is projected to increase by 2.3 to 2.6°C under RCP4.5 and by 4.4 to 4.7% under RCP8.5. For both scenarios, a decline in precipitation is projected while temperature is expected to increase. The Palmer Drought Severity Index (PDSI) revealed that the Souss watershed, characterized by incipient drought during the historical period, will experience mild and moderate drought in 2066–2095 under the RCP4.5 and RCP8.5 scenarios, respectively.
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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 | 0.092 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".