Coupling simple dry physics to a dynamically adaptive global atmosphere model
Bibliographic record
Abstract
Adaptive global circulation models (GCMs) have the potential to significantly improve the computational efficiency and accuracy of climate simulations by dynamically adjusting the local grid resolution to ensure a specified numerical tolerance or to track features of interest. We have developed the global dynamical cores WAVETRISK-ATMOSPHERE and WAVETRISK-OCEAN to explore the strengths and weaknesses of dynamical GCMs. The main open challenge of adaptive climate modelling is how to appropriately couple the dynamical core to the physics. The physics should ideally be “scale-aware”: adjusting the parameterization as necessary based on the current local resolution (or disabling it entirely if the physical phenomenon becomes fully resolved). A related question is whether the grid adaptation criteria should be based on the physics as well as the dynamics. Such scale-aware physics parameterizations remain poorly understood. In this talk we report on initial progress in coupling WAVETRISK-ATMOSPHERE to Hourdin’s (1992) “simple dry physics”.A better understanding of scale-aware physics will also improve non-adaptive climate modelling, since such models currently require extensive tuning each time the resolution is increased. An additional goal of this project is to develop a set of test cases for the simple physics that could be used to compare dynamical cores using a well-understood and standardized physics package.This is joint work with Gabrielle Ching-Johnson (MSc student, McMaster University, Canada) and Thomas Dubos (LMD, École Polytechnique, France)
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".