Isentropic mixing vs. convection in CLaMS-3.0/MESSy: evaluation using satellite climatologies and in situ carbon monoxide observations
Bibliographic record
Abstract
Lagrangian modeling of transport, as implemented in the Chemical Lagrangian Model of the Stratosphere (CLaMS), connects the advective (reversible) component of transport along 3D trajectories with mixing, the irreversible component. Here, we investigate the interplay between strongly localized convective uplifts and large-scale flow dynamics in the upper troposphere and lower stratosphere (UTLS). We revisit the Lagrangian formulation of convection in CLaMS-3.0/MESSy, driven by ECMWF's ERA5 reanalysis, and further develop the model. These developments include refining spatial resolution in the Planetary Boundary Layer (PBL) and decoupling the frequency of the adaptive grid procedure – which captures isentropic mixing and redefines Lagrangian air parcels – from the parameterization of convection. To improve the model's UTLS transport representation, particularly from the PBL over days to weeks, we derive zonally and seasonally resolved climatologies of CO partial columns (XCO, spanning 147–68 hPa) and compare them with Microwave Limb Sounder (MLS) and Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) observations, as well as in situ data. Incorporating a parameterization for unresolved convection significantly improves CO anomaly representation in the UTLS, particularly in capturing seasonal and spatial patterns. While the simulated absolute XCO values align better with ACE-FTS, the model reproduces MLS anomalies more accurately, suggesting MLS better represents CO variability. In situ observations in the boreal polar region generally support lower ACE-FTS CO values, while MLS better represents CO enhancements in air affected by the Asian summer monsoon above 10 km.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".