Selecting Observationally Constrained Global Climate Model Ensembles Using Autoencoders and Transfer Learning
Bibliographic record
Abstract
Abstract Climate modes of variability are recurring patterns that influence climate phenomena across spatial scales. Accurately representing these modes in Global Climate Models (GCMs) is crucial for assessing model performance and reducing uncertainty in future climate projections. In this study, we present a novel approach utilizing autoencoder neural networks (AEs) combined with transfer learning to evaluate the representation of monthly sea level pressure (SLP) modes over North America across five GCMs: the Geophysical Fluid Dynamics Laboratory Climate Model (GFDL‐CM4), Centro Euro‐Mediterraneo sui Cambiamenti Climatici Climate Model (CMCC‐CM2‐SR5), Canadian Earth System Model Version 5 (CanESM5), Institut Pierre‐Simon Laplace Climate Model (IPSL‐CM6A‐LR), and Hadley Center Global Environment Model Version 3 (HadGEM3‐GC31‐LL). We derived the reference regional SLP modes using autoencoders (AE) from the European Center for Medium‐Range Weather Forecasts Reanalysis (ERA5), capturing more physically consistent SLP patterns. Transfer learning was employed to adapt the pre‐trained AE, from ERA5 to the GCM outputs, enabling a direct and robust evaluation of each model’s ability to produce the observationally constrained SLP modes. This approach allowed us to rank the GCMs based on how well they replicated the reference SLP modes, providing an observationally constrained assessment of model performance. The congruence coefficients between the modeled and reference modes exceeded 0.91 for all GCMs, demonstrating strong performance in simulating regional SLP modes over North America. Among the models, HadGEM3‐GC31‐LL achieved the highest performance with an average congruence coefficient of 0.94. These results highlight the effectiveness of neural network techniques in evaluating and ranking GCMs for model intercomparison projects.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".