Analyzing the Future Monthly Precipitation Pattern in Bangladesh from Multi-Model Projections Using Both GCM and RCM
Bibliographic record
Abstract
It is very much essential to comprehend the inter-relationship of future possible trend of precipitation with the water-stress problems in a small country like Bangladesh, since enormous challenges associated with water supply are already present in this region. A major dimension of climate change for Bangladesh includes the expectation of more intensive and variability of precipitation events in future times. There are a number of mathematical models of global circulation that indicate expectations of future climate scenarios. But one particular model does not produce a perfect projection of future climatology or observations as the inherent physics and associated underlying assumptions of the model-components might be different for different climate models. As such, it is best to combine several climate models to enable a choice to produce the most appropriate projection to be used in climate-scenario generation for a small geographical area. This paper features the development of Multi-Model combination of future precipitation projections for Bangladesh on monthly basis, for each of the year from 2011 to 2100, using both global and regional climate models. Four selected IPCC ensemble Global Climate Models (GCMs), namely CGCM3.1, CCSM3, MIROC3.2 and HadGEM1 as well as a Regional Climate Model (RCM) called PRECIS have been applied in this regard. The multi-model average precipitation changes for Bangladesh at SRES A1B scenario indicate that the precipitation might continue to increase in all the months in future years. Percentage of precipitation increment is expected to be quite higher for dry and pre-monsoon months compared to the monsoon season. Also, the large scatters in the projected precipitation quantities of July and in most other monsoon months are noted, indicating that there will be years with more or less rainfall, with the variations representing significant fluctuations from average conditions.
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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 | 0.000 | 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) | 0.001 | 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 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".