Impacts of spatial resolutions on projected changes in precipitationextremes: from site- to grid-scales
Bibliographic record
Abstract
Abstract. Precipitation extremes are localized and spatially heterogeneous events. Magnitude of precipitation extreme p is expected to be spatial resolution dependent. Heavy precipitation extremes tend to be less intensive at coarser resolutions due to the averaging effect of the neighbouring less extreme events. Given the resolution dependent p, this study aims to investigate how spatial resolutions affect projected changes in precipitation extremes between future and historical periods, i.e. pfut − phis, which is a commonly used metric in climate projections. Our results show that although p is sensitive to spatial resolutions, differences in pfut − phis among various spatial resolutions are relatively small. Assessments of performances of GCMs in simulating p and pfut − phis are conducted based on three commonly used strategies that account for differences in spatial resolutions between GCMs and observations, i.e. the site-scale, the grid-scale, and the grid-point (i.e. direct comparison of grid-against scale-extremes) comparisons. Performances of GCMs in the site-scale comparison outperform those in the grid-scale and grid-point comparisons, because the statistical downscaling method incorporates the site-scale information to the future values when downscaling the GCMs. Assessment results of the grid-point comparison are comparable to those of the grid-scale comparison, even though the former has been criticized for not accounting for the difference in spatial resolutions between GCMs and observations. The spatial distributions of pfut − phis under RCP8.5 show that their differences between the site scale and the GCMs’ original resolutions are marginal. Given the considerable discrepancies among GCM outputs, the effects of spatial resolutions on projected changes are negligible.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".