Temperature and rainfall extremes change under current and future global warming levels across Indian climate zones
Bibliographic record
Abstract
Mean surface temperature is projected to rise by about 4.4 °C by the end of the century compared to the period between 1976 and 2005 when following the most extreme scenario of the greenhouse gas emissions pathway (Krishnan et al., 2020). With this rise in mean temperature, there is a lot of uncertainty on how weather and climate extremes would unfold, especially for various climate zones of India. It is therefore essential that the potential changes in both magnitude and direction of weather and climate extremes at the regional level when the global temperature reaches the different warming levels from 1 °C to 3 °C be established to allow for informed policy formulation. The present study explores the potential changes in the Expert Team on Climate Change Detection and Indices of rainfall and temperature estimated from the coupled model inter-comparison project CMIP5 multi-model ensemble over different climatic zones of India at 1 °C, 1.5 °C, 2 °C, 2.5 °C and 3 °C global temperature rise relative to pre-industrial levels under two Representative Concentration Pathways, RCP4.5 and RCP8.5. Projected changes in temperature extremes indicate significant changes at all warming levels across the nine climate zones of India. Hot temperature extremes are expected to increase while cold temperature extremes decrease. For India, country average at 3 °C under the RCP8.5 and 2 °C under the RCP4.5 scenarios, ensemble median shows that Warm Spell Duration Index will increase by 131 days and 66 days; hot days increase by 44% and 52%, warm nights increase by 23% and 13%; cold days decrease by 10% and 9%, and cold nights decrease by 13% and 12% relative to pre-industrial levels. The greatest changes in temperature based indices are projected in the colder northern parts of the country followed by the arid zone. Ensemble median for rainfall indices shows an increase in high precipitation indices, though with large model spread indicating the large uncertainties in the projections. Annual total precipitation and heavy rainfall related extreme indices show statistically significant increases in the tropical, temperate and semi-arid regions of India, moving from 1 °C to 3 °C warming level under RCP8.5 scenario whereas there is generally no significant change in the maximum number of consecutive dry and wet days. Moreover, the potential changes in climate extremes at the regional level are expected to have far-reaching impacts on the social and economic statuses of the respective climate zones. This information at a regional scale also calls attention to the national and state action plan on climate change and adaptation to be more responsive in order to take coherent and integrated policy decisions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 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".