Application of SCIAMACHY and MOPITT CO total column measurements to evaluate model results over biomass burning regions and Eastern China
Bibliographic record
Abstract
Abstract. We developed a new CO vertical column density product from near IR observations of the SCIAMACHY instrument onboard ENVISAT. For the correction of a temporally and spatially variable offset of the CO vertical column densities we apply a normalisation procedure based on coincident MOPITT (version 4) observations over the oceans. The resulting normalised SCIAMACHY CO data is well suited for the investigation of the CO distribution over continents, where important emission sources are located. We use only SCIAMACHY observations for effective cloud fractions below 20 %. Since the remaining effects of clouds can still be large (up to 100 %), we applied a cloud correction scheme which explicitly considers the cloud fraction, cloud top height and surface albedo of individual observations. The normalisation procedure using MOPITT data and the cloud correction substantially improve the agreement with independent data sets. We compared our new SCIAMACHY CO data set, and also observations from the MOPITT instrument, to the results from three global atmospheric chemistry models (MATCH, EMAC at low and high resolution, and GEOS-Chem); the focus of this comparison is on regions with strong CO emissions (from biomass burning or anthropogenic sources). The comparison indicates that over most of these regions the seasonal cycle is generally captured well but the simulated CO vertical column densities are systematically smaller than those from the satellite observations, in particular with respect to SCIAMACHY observations. Because SCIAMACHY is more sensitive to the lowest part of the atmosphere compared to MOPITT, this indicates that especially close to the surface the model simulations systematically underestimate the true atmospheric CO concentrations, probably caused by an underestimation of CO emissions by current emission inventories. For some biomass burning regions, however, such as Central Africa in July–August, model results are also found to be higher than the satellite 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".