Climatological features of future mesoscale convective systems in convection‐permitting climate models using CMIP6 and ERA5 in the central United States
Bibliographic record
Abstract
Abstract Motivated by the limited understanding of future changes in mesoscale convective systems (MCSs), we investigated characteristics of warm‐season (June–August) MCSs in the central United States based on high‐resolution convection‐permitting Weather Research and Forecasting simulations. We examined two 15‐year simulations, which include current simulations (2004–2018) forced by European Centre for Medium‐Range Weather Forecasts Reanalysis version 5 (ERA5) and future simulations (2086–2100) forced by perturbed ERA5 (i.e., ERA5 plus climate change signal derived from 28 Coupled Intercomparison Projected Phase 6 models under the Shared Socioeconomic Pathway–Representative Concentration Pathway 8.5 emission scenario). The initiations and longevities of MCSs were determined using the object‐tracking algorithm MODE‐Time Domain (MTD) from observation, current simulations (ERA), and future simulations (pseudo‐global warming, PGW). Objects identified by MODE‐Time Domain were divided into short‐/long‐lived (based on 75th percentiles of longevity) and daytime (initiated during 0000–1100 UTC)/nighttime (initiated during 1200–2300 UTC). We found that ERA and observation have comparable occurrences of MCSs. MCSs in PGW are associated with intensified rain rates in New Mexico, Colorado, and Kansas and lower rain rates in Texas, Louisiana, and Arkansas than in ERA. Moreover, the statistical analysis based on 15 parameters before MCSs initiation indicates that short‐lived MCSs in PGW are characterized by prominent changes in precipitable water (PW) and the most unstable convective available potential energy. We also found that long‐lived MCSs in PGW are associated with prominent changes in PW, unstable convective available potential energy, and isentropic potential vorticity at 345 K. According to the statistical results, PW is the most important variable in determining the longevity of MCSs and in understanding future changes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".