Trends and variability in Temperature profiles taken from Lauder, New Zealand
Bibliographic record
Abstract
Satellite and radiosonde measurements have shown that in the last few decades, globally, mean stratospheric temperatures have decreased. Cooling of the stratosphere is predominantly driven by anthropogenic $CO_2$ emissions and by decreasing stratospheric ozone concentrations from the 1980's. In the last decade, the stratospheric temperature appears to flatten of. This is suspected to be due to regulations under the Montreal protocol, restricting the emissions of ozone depleting substances and thereby limiting the ozone component in stratospheric cooling. However, research regarding the quantification of the separate contributions of ozone and $CO_2$ to the cooling is limited. This report describes and analyses temperature profile time series taken by a Lidar instrument, situated in Lauder, New Zealand. It is shown that the Lauder observations contain temperature trends in the upper stratosphere, where increased $CO_2$ abundance contributes to -0.4 to -1.5 K/dec cooling. In the lower stratosphere, $CO_2$ component varies from -0.3 to + 0.4 K/dec. Changes in ozone column above Lauder are small, causing ozone to contribute to a trend of approximately +0.1 K/dec. Additionally, the observations were compared with other lidar and sonde measurements. Observations at higher latitudes predominantly show stronger temperature trends than the Lauder observations, varying from a 0.5 to almost 2 K/dec of $CO_2$ induced cooling. Ozone columns, which are shown to recover at rates of 2\\% per decade induce a positive temperature trend of up to 0.2 to 0.6 K/dec. Finally, it is shown that models used in the Coupled Model Intercomparison Project 5 (CMIP5) give adequate simulations of temperature trends in the Stratosphere with respect to the observations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".