Global and regional climate responses to national-committed emission reductions under the Paris agreement
Bibliographic record
Abstract
To stabilize global mean temperature change within the range of 1.5–2.0°C in accordance with the Paris Agreement, countries worldwide submitted their Intended Nationally Determined Contributions with their proposed emission reductions. However, it remains unclear what the resulting climate change in terms of temperature and precipitation would be in response to the Intended Nationally Determined Contribution emission efforts. This study quantifies the global and regional temperature and precipitation changes in response to the updated Intended Nationally Determined Contribution scenarios, using simulations of 14 Fifth Coupled Climate Model Intercomparison Project models. Our results show that Intended Nationally Determined Contribution emissions would lead to a global mean warming of 1.4°C (1.3–1.7°C) in 2030 and 3.2°C (2.6–4.3°C) in 2100, above the preindustrial level (the 1850–1900 average). Spatially, the Arctic is projected to have the largest warming, 2.5 and 3 times the global average for 2030 and 2100, respectively, with strongest positive trends at 70–85°N over Asia, Europe and North America (6.5–9.0°C). The excessive warming under Intended Nationally Determined Contribution scenarios is substantially above the 1.5°C or 2.0°C long-term stabilization level. Global mean precipitation is projected to be similar to preindustrial levels in 2030, and an increase of 6% (4–9%) by 2100 compared with the preindustrial level. Regional precipitation changes will be heterogeneous, with significant increases over the equatorial Pacific (about +120%) and strong decreases over the Mediterranean, North Africa and Central America (−15% – −30%). These results clearly show that it is necessary to adjust and strengthen national mitigation efforts on current Intended Nationally Determined Contributions to meet the long-term temperature target.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".