Current status of the ability of the GEMS/MACC models to reproduce the tropospheric CO vertical distribution as measured by MOZAIC
Bibliographic record
Abstract
Abstract. Vertical profiles of CO taken from the MOZAIC aircraft database are used to present (1) a global analysis of CO seasonal averages and interannual variability for the years 2002–2007 and (2) a global validation of CO estimates produced by the MACC models for 2004, including an assessment of their ability to transport pollutants originating from the Alaskan/Canadian wildfires. Seasonal averages and interannual variability from several MOZAIC sites representing different regions of the world show that CO concentrations are highest and most variable during the winter season. The inter-regional variability is significant with concentrations increasing eastward from Europe to Japan. The impact of the intense boreal fires, particularly in Russia, during the fall of 2002 on the Northern Hemisphere CO concentrations throughout the troposphere is well represented by the MOZAIC data. A global validation of the GEMS/MACC GRG models which include three stand-alone CTMs (MOZART, MOCAGE and TM5) and the coupled ECMWF Integrated Forecasting System (IFS)/MOZART model with and without MOPITT CO data assimilation show that the models have a tendency to underestimate CO. The models perform best in Europe and the US where biases range from 0 to –25% in the free troposphere and from 0 to –50% in the surface and boundary layers (BL). The biases are largest in the winter and during the daytime when emissions are highest, indicating that current inventories are too low. Data assimilation is shown to reduce biases by up to 25% in some regions. The models are not able to reproduce well the CO plumes originating from the Alaskan/Canadian wildfires at downwind locations in the eastern US and Europe, not even with assimilation. Sensitivity tests reveal that this is mainly due to deficiencies in the fire emissions inventory and injection height.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".