Assessment of Maximum Temperature for Future Time Series over Aurangabad, Maharashtra State, India
Bibliographic record
Abstract
Climatic downscaling is an important tool used by many researchers in the field of climate to downscale climatic data available for global area to study the impacts of climate on smaller area. In climatic downscaling, there are mainly two types: 1) Statistical Downscaling and 2) Dynamical Downscaling. In the present study, statistical downscaling has been performed using SDSM (Statistical Downscaling Model) to find out future values of maximum temperatures (Tmax) over Aurangabad region (Latitude: 19.8911° N, Longitude: 75.1545° E). For this downscaling, CanESM2 (Canadian Earth System Model) CMIP5 (Coupled Model Intercomparison Project Phase 5) GCM (General Circulation Model) were selected and downscaling was performed under three different RCPs (Representative Concentration Pathways): RCP 2.6, RCP 4.5 and RCP 8.5. Downscaling model is calibrated and validated successfully over the baseline period 1961-2005 and results are presented graphically as well as statistically. Future downscaling values of maximum temperatures are presented with the help of three future time series: 2020s (2011-2040), 2050s (2041-2070) and 2080s (2071-2099) and it is observed that there is increase in the values of Tmax for the future time series with respect to the baseline period. Further the heat maps are developed for increasing monthly mean daily Tmax values under three RCPs with respect to the baseline period over the duration 2006 to 2013 and compared with increased observed monthly mean daily Tmax values over the same duration. This comparison has been done to assess the pattern of increasing monthly mean daily Tmax of downscaling results over Aurangabad region. All three RCPs are showing increasing values of monthly mean daily Tmax values for the selected three future time series with respect to the baseline period. Decade wise study of downscaling results is also showing increasing values of mean monthly daily Tmax with respect to the baseline period. Tmax values are predicted to be increased under RCP 2.6 by 1.10 0C, under RCP 4.5 by 2.13 0C and under RCP 8.5 by 4.20 0C at the end of 2099 which follows the prediction given by IPCC for global level temperature rise up to 2100.
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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 | 0.000 |
| 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 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".