ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative\n Transfer in Weather and Climate Models
Bibliographic record
Abstract
Numerical simulations of Earth's weather and climate require substantial\namounts of computation. This has led to a growing interest in replacing\nsubroutines that explicitly compute physical processes with approximate machine\nlearning (ML) methods that are fast at inference time. Within weather and\nclimate models, atmospheric radiative transfer (RT) calculations are especially\nexpensive. This has made them a popular target for neural network-based\nemulators. However, prior work is hard to compare due to the lack of a\ncomprehensive dataset and standardized best practices for ML benchmarking. To\nfill this gap, we build a large dataset, ClimART, with more than \\emph{10\nmillion samples from present, pre-industrial, and future climate conditions},\nbased on the Canadian Earth System Model. ClimART poses several methodological\nchallenges for the ML community, such as multiple out-of-distribution test\nsets, underlying domain physics, and a trade-off between accuracy and inference\nspeed. We also present several novel baselines that indicate shortcomings of\ndatasets and network architectures used in prior work. Download instructions,\nbaselines, and code are available at: https://github.com/RolnickLab/climart\n
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".