An EMD and PCA hybrid approach for separating noise from signal, and signal in climate change detection
Bibliographic record
Abstract
Abstract One of the important issues in climate change detection is the selection of climate models for the background noise. The background noise is generally chosen in a somewhat subjective manner. In the current study, we propose an approach of detecting climate change signal in order to mitigate the effects of background noise and to improve climate change detection ability. At first, the high‐frequency components of three climate datasets (climate signal, observation, background noise) induced from the random noise process are extracted from empirical mode decomposition (EMD) analysis. Then, statistical detection techniques are applied to the datasets from which the high‐frequency random components are excluded. The proposed approach is tested with synthetically generated data and with a real‐world case study represented by global surface temperature anomaly (GSTA) data. The case study reveals that each component of the observed GSTA data from EMD contains the information related to external and internal forcings such as solar activity and oceanic circulation. Among these components, the statistically significant low‐frequency components are employed in climate change detection. Compared to one of the existing approaches, some improvements in the slope coefficient estimates and the signal‐to‐noise ratio (SNR) are observed in the synthetic application of the proposed model. The application to the GSTA data shows higher SNR in the proposed approach than in the existing approach. Copyright © 2011 Royal Meteorological Society
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".