Climate Modelling at the University of Victoria
Bibliographic record
Abstract
The climate modelling group at the University of Victoria is involved in research in the fields of climate and paleoclimate modelling, dynamics and variability using a coupled atmosphere, ocean and sea ice model. Typically the computational cost of running such models prohibits running them to equilibrium. For this reason a model was recently developed at the University of Victoria which simplifies the atmospheric component of the climate system by representing it as an energy-moisture balance in a single layer. Furthermore the coupled model does not require flux adjustments to reach a stable equilibrium. This simplified model, used in concert with the GFDL Modular Ocean Model, allows us to perform a wide range of experiments and sensitivity analyses. Further recent enhancements include the addition of a sophisticated thermodynamic and dynamic ice model, snow, effects of orography on precipitation and a continental ice sheet model. A new model grid has also been developed which shifts the North Pole of the model into Greenland to improve the representation of the Arctic Ocean. All of the coupled model experiments as well as analysis and visualisation software are run on a suite of RS6000 computers which includes two IBM SP systems. Recent research in our group includes the following experiments: an examination of the role of the parameterisation of sub-grid-scale mixing in the ocean in the context of CO2 doubling and quadrupling experiments; and a paleoclimatic experiment to examine the role in the climate system of atmospheric carbon dioxide and geographic relocation of the continents 440 million years ago. Further studies include analysis of the climate of the last glacial maximum as well as an examination of wind-driven variability in the North Atlantic thermohaline circulation and the Arctic sea ice cover.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".