Parameterizations: representing key processes in climate models without resolving them
Bibliographic record
Abstract
Abstract A basic requirement of climate models is to account for the effects of processes that cannot be represented in spatial or temporal detail because of limitations imposed by resolution or other modeling considerations. Such parameterizations specify an average or expected effect of such processes on the resolved variables. This has traditionally been formulated in a deterministic way in terms of the resolved variables as the mean effect averaged across many realizations of the small scales with the same large‐scale situation, implicitly or explicitly assuming the existence of some equilibrium state as a closure condition. More recently, the uncertainty of such closure assumptions has led to the use of stochastic forms of parameterization, where the required effects on the resolved scale are determined from a set of randomly chosen realizations of unresolved processes that have a known probability of occurrence given the resolved state. Theoretical and practical approaches to parameterization are discussed and illustrated with selected examples. New directions that employ hybrid modeling strategies and stochastic methods to overcome well‐known parameterization difficulties are discussed. WIREs Clim Change 2011 2 482–497 DOI: 10.1002/wcc.122 This article is categorized under: Climate Models and Modeling > Model Components
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".