Evaluation of the Performance of <scp>HighResMIP CMIP6</scp> in Simulating Extreme Precipitation in Madagascar
Bibliographic record
Abstract
ABSTRACT This study assesses the performance of 17 High‐Resolution Model Intercomparison Project (HighResMIP) from the Coupled Model Inter‐comparison Project Phase 6 (CMIP6) and their ensemble mean in simulating extreme precipitation in Madagascar. For this purpose, nine extreme precipitation indices were used, namely consecutive dry days (CDD), consecutive wet days (CWD), heavy precipitation days (R10mm), very heavy precipitation days (R20mm), simple daily intensity (SDII), maximum 1‐day precipitation (RX1day), maximum 5‐day precipitation (RX5day), very wet days (R95P) and extremely wet days (R99p). Furthermore, two gridded data sets, the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and ERA5, were employed as the reference data. The performance of the models was assessed using Normalised Root Mean Squared Error (NRMSE), percentage of bias (PBIAS) and Taylor Skill Score (TSS). In addition, this study used a comprehensive model ranking (MR) to provide an inclusive assessment of the models. The results suggest that most HighResMIP models fairly reproduce the precipitation climatology in Madagascar. We also found that except for the simulation of R99P, most of the models have satisfactorily good results in simulating extreme precipitation indices over the study area. A further comparison between HighResMIP models and their CMIP6 counterparts showed that most of the high‐resolution models have better performance; yet improving model parametrization is still important. In line with previous research, by crossing the results from CHIRPS and ERA5, we found that the multi‐model mean outperformed individual models. Nevertheless, individual models such as HadGEM3‐GC31‐HH, HadGEM3‐GC31‐HM, ECMWF‐IFS‐HR and EC‐Earth3P‐HR have relatively good results. On the contrary, the worst performance is attributed to HIRAM‐SIT‐LR and INM‐CM5‐h. The outputs and results from this research deliver a comprehensive assessment of the performance of the new HighResMIP in simulating precipitation extremes at a local study, which are essential for policymakers and climate modellers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".