Estimating Climate Feedbacks Using a Neural Network
Bibliographic record
Abstract
Abstract A nonlinear method has been developed to estimate climate feedbacks based on the Neural Network (NN) taking advantage of its self‐learning skills. The NN model developed here is trained using a reanalysis data set and predicts radiation flux globally from atmospheric and surface variables. The radiative feedbacks of temperature, water vapor, surface albedo, and cloud in the interannual climate variations estimated from the NN method are in agreement with those from a broadly used kernel method. However, the NN method demonstrates significant advantages: (1) it withdraws the linearity assumption of the kernel method and accounts for the nonlinear effects of the feedbacks. In the case of large climate perturbations, such as that in the Arctic caused by sea ice melt, the NN method achieves better radiation closure. (2) The method can directly calculate the radiative feedback of cloud and its components. We find that the high, middle, and low cloud feedback components analyzed from the NN method are linearly additive in the interannual climate variations, although there is a considerable nonlinear effect arising from the interactions between cloud and noncloud variables.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".