Development of Statistically Downscaled Regional Climate Model based on Representative Concentration Pathways for Ipoh, Subang and KLIA Sepang in Peninsular Malaysia
Bibliographic record
Abstract
Abstract Climate change is one of the challenging global issues that our world is facing and it is intensely debated on the international agenda. It is a fact that climate change has brought about many disastrous events on a global scale which affect our livelihoods. Climate models are commonly used by researchers to study the magnitude of the changing climate and to simulate future climate projections. Most climate models are developed based on various interactions among the Earth’s climate components such as the land surface, oceans, atmosphere and sea-ice. In this study, the second-generation Canadian Earth System Model (CanESM2) was statistically downscaled to develop a regional climate model (RCM) based on three representative concentration pathways (RCPs): RCP2.6, RCP4.5 and RCP8.5. The RCM will be used to simulate the average minimum and maximum temperatures and average precipitation for Ipoh, Subang and KLIA Sepang in Peninsular Malaysia for the years 2006 to 2100. The simulated data were bias corrected using the historical observation data of monthly average minimum and maximum temperatures and monthly average rainfall retrieved from the Malaysian Meteorological Department (MMD). The different trends of the simulated data for all the three locations based on the RCP2.6, RCP4.5 and RCP8.5 were evaluated for future climate projection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".