Long-term dynamics of OH * temperatures over central Europe: trends and solar correlations
Bibliographic record
Abstract
Abstract. We present the analysis of annual average OH* temperatures in the mesopause region derived from measurements of the Ground-based Infrared P-branch Spectrometer (GRIPS) at Wuppertal (51° N, 7° E) in the time interval 1988 to 2015. The new study uses a temperature time series which is 7 years longer than that used for the latest analysis regarding the long-term dynamics. This additional observation time leads to a change in characterisation of the observed long-term dynamics. We perform a multiple linear regression using the solar radio flux F10.7 cm (11-year cycle of solar activity) and time to describe the temperature evolution. The analysis leads to a linear trend of (−0.089 ± 0.055) K year−1 and a sensitivity to the solar activity of (4.2 ± 0.9) K (100 SFU)−1 (r2 of fit 0.6). However, one linear trend in combination with the 11-year solar cycle is not sufficient to explain all observed long-term dynamics. In fact, we find a clear trend break in the temperature time series in the middle of 2008. Before this break point there is an explicit negative linear trend of (−0.24 ± 0.07) K year−1, and after 2008 the linear trend turns positive with a value of (0.64 ± 0.33) K year−1. This apparent trend break can also be described using a long periodic oscillation. One possibility is to use the 22-year solar cycle that describes the reversal of the solar magnetic field (Hale cycle). A multiple linear regression using the solar radio flux and the solar polar magnetic field as parameters leads to the regression coefficients Csolar = (5.0 ± 0.7) K (100 SFU)−1 and Chale = (1.8 ± 0.5) K (100 µT)−1 (r2 = 0.71). The second way of describing the OH* temperature time series is to use the solar radio flux and an oscillation. A least-square fit leads to a sensitivity to the solar activity of (4.1 ± 0.8) K (100 SFU)−1, a period P = (24.8 ± 3.3) years, and an amplitude Csin = (1.95 ± 0.44) K of the oscillation (r2 = 0.78). The most important finding here is that using this description an additional linear trend is no longer needed. Moreover, with the knowledge of this 25-year oscillation the linear trends derived in this and in a former study of the Wuppertal data series can be reproduced by just fitting a line to the corresponding part (time interval) of the oscillation. This actually means that, depending on the analysed time interval, completely different linear trends with respect to magnitude and sign can be observed. This fact is of essential importance for any comparison between different observations and model simulations.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".