Influence of Horizontal Model Resolution on the Horizontal Scale of Extreme Precipitation Events
Bibliographic record
Abstract
A fundamental characteristic of extreme precipitation events (EPEs) is their horizontal scale. This horizontal scale can influence the intensity of an EPE through its effect on the timescale of an EPE as well as its effect on the strength of convective feedbacks. Thus, to have confidence in future projections of extreme precipitation, the horizontal scales of EPEs in global climate models (GCMs) should be evaluated. Analyzing daily output from 27 models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6), including 13 models participating in the High Resolution Model Intercomparison Project (HighResMIP), we computed the horizontal scales of EPEs and extreme ascent for annual maximum EPEs during 1981-2000. We found that the horizontal scales of both EPEs and the associated ascending motion are resolution-dependent: for a factor of seven increase in horizontal resolution, the horizontal scale decreases by a factor of approximately two to five, with higher sensitivity in the tropics than in the midlatitudes. Further analysis in the southern hemisphere midlatitudes reveals that this resolution dependence results from precipitation during the simulated EPEs that is almost entirely resolved rather than parameterized. However, the EPEs are not simply grid box storms, and analysis of the horizontal scales of geopotential anomalies suggests that the planetary-scale dynamics in GCMs is not resolution-dependent. Thus, the dominance of resolved precipitation during EPEs is more likely due to convection on the model grid or formation of strong, poorly resolved fronts, and additional work is needed to explore these possibilities and find a remedy for this resolution dependence.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".