Impacts of 1.5 °C and 2 °C global warming on regional rainfall and temperature change across India
Bibliographic record
Abstract
Abstract The participating member nations in Paris at the 2015 convention of the United Nations Framework Convention on Climate Change (UNFCCC) resolved to maintain the rise in global average temperature to a level much less than 2.0 °C compared to pre-industrial levels. It was also committed that the parties would continue with all-out endeavor to limit warming to 1.5 °C. For a country like India with a primarily agrarian economy this leads to two key questions. Firstly, what does the global rise of mean annual temperature (1.5 °C and 2.0 °C) mean at the regional scale? Secondly, what are the implications of keeping warming at or below 1.5 °C for different sectors and in particular on agriculture and water resources? To address these questions we have examined the annual and seasonal impacts of 1.5 °C and 2 °C global temperature rise (GTR) on temperature and rainfall change over all the states of India under two Representative concentration pathways, RCP 8.5 and RCP 4.5, using all Coupled Model Inter Comparison Project CMIP5 Models. Rainfall is projected to increase over all the states with very low change in the western part of the country and highest change in the North eastern and southern region of the country under RCP 8.5. 35% of the country is projected to witness a temperature change equal to or lesser than global mean temperature of 1.5 °C and 2.0 °C whereas 65% is expected to show a greater rise in temperature. The most severe temperature change is expected to be witnessed by the presently colder Northern most states of India such as Jammu and Kashmir, Himachal Pradesh and Uttaranchal (2.0 °C to 2.2 °C at 1.5 °C and 2.5 °C to 2.8 °C at 2.0 °C) in both RCPs. There are opportunities and threats due to climate change and it is imperative for researchers and policy makers to recognize these in the context of the scenarios of 1.5 °C and 2.0 °C global temperature changes. It is essential for the current national and state action plan on climate change and adaptation to be more sensitive in strategizing an efficient response to the different scenarios at the global level (3 °C, 2 °C and 1.5 °C) in order to take more informed policy decisions at global level in synergy with the regional analysis to be able to develop strategies that benefit the local populace.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".