The influence of stratospheric temperature changes on ozone trends: \nAnalysis of OMI ozone products and improvements for the differential \noptical absorption spectroscopy (DOAS) technique that is applied to OMI \nsatellite measurements.
Bibliographic record
Abstract
The objectives of this study are 1. to validate the OMDOAO3 fitted effective temperature; 2. to estimate the \ntemperature sensitivity of OMI ozone products and 3. improve the DOAS concept such that an ozone trend can \naccurately be determined with DOAS obtained vertical ozone column amounts. \nIt has been determined that an ozone retrieval that is able to capture ozone trends with an accuracy of 1%, \nneeds to have a temperature sensitivity of approximately 0.01%/K or less. For the Brewer direct sun observations, \nthe temperature sensitivity is estimated with the latest ozone absorption cross sections from Serdyuchenko as \n0.014+/-0.003%/K. Hence that the Brewer direct sun observations are not sensitive to changes in effective ozone \ntemperature, which is also observed with 1998-1999 Toronto measurements in a study of [1, Kerr, J. B., 2002] , \nand can therefore be used to validate OMI ozone products. \nThe ozone profile from OMO3PR is cross-validated with other ozone profiles in a study of [2, M. Kroon et al., \n2011] and therefore an ozone effective temperature can be accurately determined with an ozone profile from \nOMO3PR and a temperature profile from medium-range weather forecasting model (ECMWF). This effective \ntemperature can be used as a reference effective ozone temperature, to validate the OMDOAO3 fitted effective \ntemperature, and to estimate temperature sensitivities of OMI ozone products. For OMDOAO3 the effective temperature \nis fitted from the spectrum itself. It is found that the fitted effective temperature from OMDOAO3 has \nan offset of -5.82+/-0.04 C, which is consistent for different seasons and regions. This proves the concept that \nit is possible to retrieve an ozone effective temperature from the spectrum itself with the differential optical absorption \nspectroscopy (DOAS) technique, which in principle should give an ozone column amount independent of \ntemperature. \nFor OMTO3, the other OMI ozone column amount product, temperature profiles are used inconsistently with \nthe ozone profiles. The TOMS v8 climatology for temperature profiles depends on month and latitude, and therefore \nerrors in effective ozone temperature depending on latitude or season are not observed. However, other \nvariations in effective ozone temperature are not captured by the climatology, which are: stratospheric climate \nchange, volcanic eruptions and longitudinal temperature variations. \nFrom a cross-validation of OMI ozone products, the temperature sensitivity can be estimated for OMI ozone \nproducts, which varies between 0.06 and 0.13 %/K in absolute magnitude. This is higher than the threshold value \nof 0.01%/K for an ozone retrieval that is insensitive to temperature variations, and it can be concluded that errors \nin ozone trends can be expected on the order of 10% for OMI ozone products. \nThe temperature sensitivity can also be determined with the help of ground-based measurements. When the \ntemperature sensitivity for OMTO3 is determined, by comparing the difference in column amount to Brewer direct \nsun observations, against effective temperature difference, a temperature sensitivity of 0.255%/K is found, which \nis probably caused by longitudinal temperature variations that are not captured by the OMTO3 algorithm. For \nall OMI ozone products a temperature sensitivity is found, when the ozone column amounts are compared to the \nBrewer direct sun observations as function of temperature. This may be caused by the choice of ozone absorption \ncross sections and how its temperature dependency is implemented in the algorithm. For OMO3PR a temperature \nsensitivity of 0.0720 %/K is found, and for OMTO3 a temperature sensitivity of 0.112 %/K. For OMDOAO3 a \ntemperature sensitivity of 0.0275 %/K is found, which shows that although an effective temperature may be fitted \nfrom spectrum, the ozone column amount itself can still be sensitive to temperature variations. \n1 \nFor the current OMDOAO3 model function and fit window, the fitted effective temperature, compared to the \nmodeled slant effective temperature, has an offset of −5.8+/-1.7 C in the simulations. This is in good agreement \nwith the offset that has been found with observations for the OMDOAO3 fitted effective temperature of -5.82 +/- \n0.04 C. Hence that the found offset in effective ozone temperature can be explained by the DOAS fitting method \nitself. \nConsiderations for improving the OMDOAO3 algorithm are done, where the main improvements are 1. the use \nof all temperature expansion coefficients of the second-degree polynomial of the ozone absorption cross sections \ninstead of linearizing them - and 2. the use of a wavelength-dependent slant column amount. Simulations show that \nthe first improvement can reduce the temperature sensitivity of the algorithm by a factor of ten, which makes the \nDOAS concept a reliable source for ozone trend determination. The second improvement, the use of a wavelength \ndependent slant column amount, enables the use of 17.5 nm wide fit window, about four times as wide as the \ncurrent OMDOAO3 fit window, which can enhance noise reduction, resulting in an ozone product with a better \noverall resolution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".