Projection of important climate variables in large cities under the CMIP5–RCP scenarios using SDSM and fuzzy downscaling models
Bibliographic record
Abstract
Abstract Climate change impacts are among the many challenges facing management of large cities. This study assesses the important climate variables under climate change impacts in Tehran, Iran, for 2021–2040. Eight Coupled Model Intercomparison Project, Phase 5 (CMIP5) models under the scenarios of Representative Concentration Pathway 2.6 (RCP2.6), RCP4.5, and RCP8.5 were used, and seven climate variables were projected utilizing the Fuzzy DownScaling Model (FDSM) and the Statistical DownScaling Model (SDSM). The FDSM and SDSM results underline the high performance of both models and the important capability of the FDSM, showing the increasing trend of annual changes in mean temperature (Tmean) and maximum temperature (Tmax), precipitation, and the mean wind speed (Wmean). The maximum increase of annual average in Tmean and Tmax and the Wmean among all scenarios will be in the order of 1.29 °C, 1.57 °C, and 0.8 m/s (for RCP8.5), and also the maximum increases of annual average precipitation will be 10 mm (for RCP2.6). Furthermore, the monthly long-term averages of Tmean and Tmax in all three scenarios show significant increases in summer. For precipitation, relative stability in summer, and increases in winter and early spring are predicted, but the changes in minimum temperature, relative humidity, and sunshine hours indicate relative stability.
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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 | 0.001 | 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".