Modelling Study of the Summer Time Arctic Liquid Clouds
Bibliographic record
Abstract
Investigation of the cloud microphysics is carried out by using a fully coupled version of GEM-MACH, the Environment and Climate Change Canada’s (ECCC) online air quality forecast model, (Global Environmental Multiscale–Modelling Air quality and Chemistry) for the Arctic domain during summer 2014. Simulation results indicate that model is capable of simulating the low clouds prevailing in summertime Arctic, particularly thin water clouds (or clouds with liquid water path < 50 g m-2), which have a significant effect on cloud radiative forcing in the Arctic. Model simulations are also compared with the July 2014 NETCARE field campaign aircraft observations based from Resolute NU. The field campaign consisted of two periods with distinct metrological conditions: relatively pristine and relatively polluted Arctic atmosphere with the influence of transport from lower latitudes. For the relatively polluted period, simulations of cloud’s microphysics suggested more and smaller droplets with higher liquid water content (LWC), and hence lower precipitation and longer cloud lifetime. The model agrees well with the observation results showing that aerosols in the size range of 50-100 nm are commonly activated in the summer Arctic, with even smaller aerosols (< 50 nm) being activated during the pristine period.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".