Simulation and Projection of Global Temperature Change and Recovery of Extra-polar Ozone using Multiple Linear Regression Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Climate change and the depletion of the ozone layer are two important global environmental problems caused by the release of gases into the atmosphere. This dissertation uses multiple linear regression to quantify the natural and human components that affect Earth’s global mean surface temperature (GMST) and the thickness of the ozone layer. To analyze changes in Earth’s climate, the Empirical Model of Global Climate (EM-GC) is used to simulate and project variations in GMST. Numerous scenarios of greenhouse gas and aerosol emissions are considered to analyze the probability of achieving the 1.5 and 2.0°C warming goals set by the Paris Agreement. There is a 53% likelihood of the rise in GMST staying below 1.5°C if the world follows the greenhouse gas and aerosol emissions in SSP1-2.6, and a 64% probability of staying below 2.0°C if the world follows SSP4-3.4. The amount of warming attributed to humans from 1975 to 2014 based on the EM-GC is 0.157°C decade−1 (range of 0.120 to 0.195°C decade−1). Multi-model output from the Coupled Model Intercomparison Project Phase 6 (CMIP6) indicates humans contributed 0.221°C decade−1 (0.151 to 0.299°C decade−1) of warming from 1975 to 2014, which is notably faster warming than inferred from the historical climate record. The rise in GMST at 2×preindustrial concentrations of carbon dioxide is also examined. The effects of increasing methane emissions are discussed, as well as the timeline for emitting the remainder of the world’s carbon budget. Humans can emit another 150 ± 79 Gt C after 2019 to have a 66% likelihood of limiting warming to 1.5°C and another 400 ± 104 Gt C to have the same probability of limiting warming to 2.0°C. Given the estimated emission of 11.7 Gt C per year for 2019 due to human activities, carbon and methane emissions must be severely curtailed in the next 10 years to achieve the 1.5°C goal of the Paris Agreement. The results from the EM-GC are compared to other reduced complexity climate models (RCMs) as part of an international collaboration, as well as multi-model output from CMIP6. All RCMs, including the EM-GC, show that the CMIP6 global climate models warm too quickly. To analyze changes in the ozone layer due to human activities, a multiple linear regression model is used that includes equivalent effective stratospheric chlorine (EESC) as a measure of the human effect on ozone. Results using the updated EESC calculation indicate anthropogenic, very short-lived chlorine (VSL Cl) species not regulated by the Montreal Protocol have already caused a seven-year delay in the recovery of the ozone layer. Future simulations indicate that if human emissions of VSL Cl species continue to rise, the recovery of the ozone layer could be delayed up to 25 years.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it