The vertical resolution sensitivity of simulated equilibrium temperature and water‐vapour profiles
Bibliographic record
Abstract
Abstract Variability of atmospheric water vapour is the most important climate feedback in present climate models. Thus, it is of crucial importance to understand the sensitivity of water vapour to model attributes, such as physical parametrizations and resolution. Here we attempt to determine the minimum vertical resolution necessary for accurate prediction of water vapour. To address this issue, we have run two single‐column models to tropical radiative—convective equilibrium states and have examined the sensitivity of the equilibrium profiles to vertical resolution. Both column models produce reasonable equilibrium states of temperature and moisture. Convergence of the profiles was achieved in both models using a uniform vertical resolution of around 25 hPa. Coarser resolution leads to significant errors in both the water vapour and temperature profiles, with a resolution of 100 hPa proving completely inadequate. However, fixing the boundary‐layer resolution and altering only the free‐tropospheric resolution significantly reduces sensitivity to vertical resolution in one of the column models, in both water and temperature, highlighting the importance of resolving boundary‐layer processes. Additional experiments show that the height of the simulated tropopause is sensitive to upper‐tropospheric vertical resolution. At resolutions higher than 33 hPa, one of the models developed a high degree of vertical structure in the vapour profile, resulting directly from the complex array of microphysical processes included in the stratiform cloud parametrization, some of which were only resolved at high resolutions. This structure was completely absent at lower resolutions, casting some doubt on the approach of using relatively complicated cloud schemes at low vertical resolutions.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| 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".