A critical look at solar-climate relationships from long temperature series
Bibliographic record
Abstract
Abstract. A key issue of climate change is to identify the forcings and their relative contributions. The solar-climate relationship is currently the matter of a fierce debate. We address here the need for high quality observations and an adequate statistical approach. A recent work by Le Mouël et al. (2010) and its companion paper by Kossobokov et al. (2010) show spectacular correlations between solar activity and temperature series from three European weather stations over the last two centuries. We question both the data and the method used in these works. We stress (1) that correlation with solar forcing alone is meaningless unless other forcings are properly accounted for and that sunspot counting is a poor indicator of solar irradiance, (2) that long temperature series require homogenization to remove historical artefacts that affect long term variability, (3) that incorrect application of statistical tests leads to interpret as significant a signal which arises from pure random fluctuations. As a consequence, we reject the results and the conclusions of Le Mouël et al. (2010) and Kossobokov et al. (2010). We believe that our contribution bears some general interest in removing confusion from the scientific debate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".