Temporal and spectral cloud screening of polar winter aerosol optical depth(AOD): impact of homogeneous and inhomogeneous clouds and crystal layers onclimatological-scale AODs
Bibliographic record
Abstract
Abstract. We compared star-photometry-derived, polar winter aerosol optical depths (AODs), acquired at Eureka, Nunavut, Canada, and Ny-Ålesund, Svalbard, with GEOS-Chem (GC) simulations as well as ground-based lidar and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) retrievals over a sampling period of two polar winters. The results indicate significant cloud and/or low-altitude ice crystal (LIC) contamination which is only partially corrected using temporal cloud screening. Spatially homogeneous clouds and LICs that remain after temporal cloud screening represent an inevitable systematic error in the estimation of AOD: this error was estimated to vary from 78 to 210 % at Eureka and from 2 to 157 % at Ny-Ålesund. Lidar analysis indicated that LICs appeared to have a disproportionately large influence on the homogeneous coarse-mode optical depths that escape temporal cloud screening. In principle, spectral cloud screening (to yield fine-mode or submicron AODs) reduces pre-cloud-screened AODs to the aerosol contribution if one assumes that coarse-mode (super-micron) aerosols are a minor part of the AOD. Large, low-frequency differences between these retrieved values and their GC analogue appeared to be often linked to strong, spatially extensive planetary boundary layer events whose presence at either site was inferred from CALIOP profiles. These events were either not captured or significantly underestimated by the GC simulations. High-frequency AOD variations of GC fine-mode aerosols at Ny-Ålesund were attributed to sea salt, while low-frequency GC variations at Eureka and Ny-Ålesund were attributable to sulfates. CALIOP profiles and AODs were invaluable as spatial and temporal redundancy support (or, alternatively, as insightful points of contention) for star photometry retrievals and GC estimates of AOD.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".