 Cloud microphysics in Arctic and Antarctic environments derived from infrared emission spectroscopy
Bibliographic record
Abstract
Climate models struggle to accurately represent polar regions, particularly during polar night, when cloud cover is especially prevalent. The uncertainty budget is dominated by cloud and cloud-aerosol interactions, but the difficulty in maintaining robust field observations means a lack of long-term validation datasets for key cloud parameters. Long-term measurements of the downwelling thermal infrared (400 - 3000 cm-1) have been recorded since 2008 with an Atmosphere Emitted Radiance Interferometer (AERI) at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada (80°N, 86°W) and operated by the Canadian Network for the Detection of Atmospheric Change (CANDAC), while a similar instrument was deployed at McMurdo Station for 2016 as part of the ARM [Atmosphere Radiation Measurement] West Antarctic Radiation Experiment (AWARE) program. We analyse the downwelling infrared emission of the polar atmosphere recorded by these AERI instruments, with supplementary data from observations and models, to derive a climatology of microphysical and optical properties of clouds at Eureka (since 2008) and McMurdo (2016), including optical depth, thermodynamic phase, and liquid droplet and ice crystal effective scattering radii. A comparison of these Arctic and Antarctic cloud properties reveals an abundance of cloud morphological states at these two polar locations. This presentation will also describe the temperature dependence of cloud microphysics, seasonality in the timeseries, and the effect of inversions on cloud boundaries, as well as challenges in performing these retrievals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".