Nitric acid trihydrate nucleation and denitrification in the Arctic stratosphere
Bibliographic record
Abstract
Abstract. Nitric acid trihydrate (NAT) particles in the polar stratosphere have been shown to be responsible for vertical redistribution of reactive nitrogen (NOy). Recent observations by Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the CALIPSO satellite have been explained in terms of heterogeneous nucleation of NAT on foreign nuclei, revealing this to be an important formation pathway for the NAT particles. In state of the art global- or regional-scale models, heterogeneous NAT nucleation is currently simulated in a very coarse manner using a constant, saturation-independent nucleation rate. Here we present first simulations for the Arctic winter 2009/2010 applying a new saturation-dependent parametrisation of heterogeneous NAT nucleation rates within the Chemical Lagrangian Model of the Stratosphere (CLaMS). The simulation shows good agreement of chemical trace species with in situ and remote sensing observations. The simulated polar stratospheric cloud (PSC) optical properties agree much better with CALIOP observations than those simulated with a constant nucleation rate model. A comparison of the simulated particle size distributions with observations made using the Forward Scattering Spectrometer Probe (FSSP) aboard the high altitude research aircraft Geophysica, shows that the model reproduces the observed size distribution, except for the very largest particles above 15 μm diameter. The vertical NOy redistribution caused by the sedimentation of the NAT particles, in particular the denitrification and nitrification signals observed by the ACE-FTS satellite instrument and the in situ SIOUX instrument aboard the Geophysica, are reproduced by the improved model, and a small improvement with respect to the constant nucleation rate model is found.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".