Multi‐Model Projection of Climate Extremes under 1.5°C–4°C Global Warming Levels across Iran
Bibliographic record
Abstract
ABSTRACT This study investigates the spatial and temporal patterns of climate extremes in Iran and projects future changes using data from seven General Circulation Models (GCMs) that participated in the Coupled Model Intercomparison Project phase 6 (CMIP6). We assess the impacts of climate change under the SSP2‐4.5 and SSP5‐8.5 emission scenarios, considering global warming levels of 1.5°C, 2°C, 3°C, and 4°C above preindustrial levels. Gridded observations are derived from ground measurements, using the SYMAP algorithm at a 1/8° latitude–longitude resolution. Subsequently, statistical downscaling of GCMs is performed using the Multivariate Bias Correction (MBC) and Bias Correction Constructed Analogues with Quantile Mapping Reordering (BCCAQ) approaches. Projected changes in extreme temperature and precipitation events are evaluated using the CLIMDEX indices. The findings indicate consistent rises in annual temperatures across Iran, with temperature indices such as warm spell duration and the monthly minimum value of daily temperature exhibiting substantial increases, about twofold by the +4.0°C period. Additionally, the study highlights a potential intensification in precipitation extremes (Rx1day, Rx5day, R90p, R95p), suggesting a heightened risk of more frequent and severe floods, particularly in the western, northern, and northwestern regions. These insights underline the critical need for region‐specific adaptation strategies to address the risks associated with climate change in Iran.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".