Implementation and calibration of a stochastic multicloud convective parameterization in the NCEP <scp>C</scp>limate <scp>F</scp>orecast <scp>S</scp>ystem (CFSv2)
Bibliographic record
Abstract
Abstract A comparative analysis of fourteen 5 year long climate simulations produced by the National Centers for Environmental Predictions (NCEP) Climate Forecast System version 2 (CFSv2), in which a stochastic multicloud (SMCM) cumulus parameterization is implemented, is presented here. These 5 year runs are made with different sets of parameters in order to figure out the best model configuration based on a suite of state‐of‐the‐art metrics. This analysis is also a systematic attempt to understand the model sensitivity to the SMCM parameters. The model is found to be resilient to minor changes in the parameters used implying robustness of the SMCM formulation. The model is found to be most sensitive to the midtropospheric dryness parameter (MTD) and to the stratiform cloud decay timescale (τ30). MTD is more effective in controlling the global mean precipitation and its distribution while τ30 has more effect on the organization of convection as noticed in the simulation of the Madden‐Julian oscillation (MJO). This is consistent with the fact that in the SMCM formulation, midtropospheric humidity controls the deepening of convection and stratiform clouds control the backward tilt of tropospheric heating and the strength of unsaturated downdrafts which cool and dry the boundary layer and trigger the propagation of organized convection. Many other studies have also found midtropospheric humidity to be a key factor in the capacity of a global climate model to simulate organized convection on the synoptic and intraseasonal scales.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".