A microphysical parameterization for convective clouds in the ECHAM5 climate model: Single‐column model results evaluated at the Oklahoma Atmospheric Radiation Measurement Program site
Bibliographic record
Abstract
The microphysical parameterization used for stratiform clouds in the ECHAM5 climate model is now extended for simulations of convective clouds. The performance of the newly implemented parameterization in simulating midlatitude continental summertime convective cloud systems is evaluated in this paper at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site in Oklahoma using the single‐column mode (SCM) of ECHAM5. Three ARM intensive operating periods (IOPs), including two summer ones and a late spring one, are used for the evaluation. Results show that the SCM simulated cloud cover fraction agrees well with observations. The SCM also captures most of the precipitation events. With the new microphysical parameterization, the model performs at least as well as with the original model setup in simulating almost all the fields examined in this study. Significant improvement is shown in the simulations of outgoing longwave radiation and net incoming solar radiation at the top of the atmosphere revealing the feasibility of the new parameterization. Sensitivity studies show that a 10‐fold increase in cloud droplet number concentration significantly increases the simulated liquid water content. More interestingly, this increase in cloud droplet number leads to an increase in the total amount of precipitation in two of the three IOPs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".