The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations
Bibliographic record
Abstract
Abstract. We analyze the source code of eight coupled climate models, selected from those that participated in the CMIP5 (Taylor et al., 2012) or EMICAR5 (Eby et al., 2013; Zickfeld et al., 2013) intercomparison projects. For each model, we sort the preprocessed code into components and subcomponents based on dependency structure. We then create software architecture diagrams that show the relative sizes of these components/subcomponents and the flow of data between them. The diagrams also illustrate several major classes of climate model design; the distribution of complexity between components, which depends on historical development paths as well as the conscious goals of each institution; and the sharing of components between different modeling groups. These diagrams offer insights into the similarities and differences in structure between climate models, and have the potential to be useful tools for communication between scientists, scientific institutions, and the public.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".