Development of Multi-model Ensembles for Climate Projection
Bibliographic record
Abstract
Climate change is one of the most challenging and defining issues that has resulted in substantial societal, economic, and environmental impacts across the world. To assess the potential climate change impact, climate projections are generated with General Circulation Models (GCMs). However, the climate change signals remain uncertain and GCMs have difficulty in representing regional climate features. Therefore, comprehensive knowledge of climate change signals and reliable high-resolution climate projections are highly desired. This dissertation aims to address such challenges by developing climate projections with multi-model ensembles for climate impact assessment. This includes: i) developing multi-model ensembles to analyze global changes in all water components within the hydrological cycle and quantify the uncertainties with GCM projections; ii) development of bias correction models for generating high-resolution daily maximum and minimum temperature projections with individual GCMs and multi-model ensemble means over Canada; iii) proposing bias correction models with individual GCMs and multi-model ensemble means for high-resolution daily precipitation projections for Canada. The proposed models are capable of developing high-resolution climate projections at a regional scale and exploring the climate change signals. The reliable climate projections generated could provide valuable information for formulating appropriate climate change mitigation and adaptation strategies across the world.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".