EVALUATING SURFACE TEMPERATURE VARIABILITIES AND CLIMATE EXTREMES IN THE DIENG PLATEAU OVER THREE DECADES
Bibliographic record
Abstract
This study examines long-term surface temperature variability and climate extremes in the Dieng Plateau, Central Java, from 1991 to 2022. Despite its tropical location, the region’s unique high-altitude microclimate, with frequent frost events, has raised concerns for local agriculture, particularly potato farming. However, limited observational data has constrained in-depth assessments. To address this, we used bias-corrected ERA5 reanalysis data, calibrated using hourly observations from an Automatic Weather Station (AWS) installed in 2021. The analysis focused on climatological trends and temperature-related extreme indices following the Expert Team on Climate Change Detection and Indices (ETCCDI) framework. Our findings indicate seasonal patterns in diurnal temperatures, with JJA (June–August) exhibiting the greatest variability and the lowest night time temperatures, conditions favorable to frost formation. Among the extreme indices, warmest night temperatures (TNx) increased significantly at a rate of 0.017°C/year (p < 0.01), while coldest night temperatures (TNn) showed a slight but significant decline. The frequency of warm nights (TN90p) rose by 0.242 days/month, while cold nights (TN10p) decreased by 0.161 days/month. Meanwhile, trends for warm days (TX90p), cold days (TX10p), and cold spell duration (CSDI) were statistically insignificant. These results highlight the plateau’s sensitivity to night time warming and the potential risk of climate-driven shifts in frost occurrence. The combination of high-resolution reanalysis data and extreme indices offers valuable insight into microclimate behavior in tropical highlands, with direct implications for frost risk management and climate adaptation strategies in vulnerable agricultural zones.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".