Aerosol and cloud microphysics covariability in the northeast Pacific boundary layer estimated with ship‐based and satellite remote sensing observations
Bibliographic record
Abstract
Abstract Ship measurements collected over the northeast Pacific along transects between the port of Los Angeles (33.7°N, 118.2°W) and Honolulu (21.3°N, 157.8°W) during May to August 2013 were utilized to investigate the covariability between marine low cloud microphysical and aerosol properties. Ship‐based retrievals of cloud optical depth ( τ ) from a Sun photometer and liquid water path (LWP) from a microwave radiometer were combined to derive cloud droplet number concentration N d and compute a cloud‐aerosol interaction (ACI) metric defined as ACI CCN = ∂ ln( N d )/∂ ln(CCN), with CCN denoting the cloud condensation nuclei concentration measured at 0.4% (CCN 0.4 ) and 0.3% (CCN 0.3 ) supersaturation. Analysis of CCN 0.4 , accumulation mode aerosol concentration ( N a ), and extinction coefficient ( σ ext ) indicates that N a and σ ext can be used as CCN 0.4 proxies for estimating ACI. ACI CCN derived from 10 min averaged N d and CCN 0.4 and CCN 0.3 , and CCN 0.4 regressions using N a and σ ext , produce high ACI CCN : near 1.0, that is, a fractional change in aerosols is associated with an equivalent fractional change in N d . ACI CCN computed in deep boundary layers was small (ACI CCN = 0.60), indicating that surface aerosol measurements inadequately represent the aerosol variability below clouds. Satellite cloud retrievals from MODerate‐resolution Imaging Spectroradiometer and GOES‐15 data were compared against ship‐based retrievals and further analyzed to compute a satellite‐based ACI CCN . Satellite data correlated well with their ship‐based counterparts with linear correlation coefficients equal to or greater than 0.78. Combined satellite N d and ship‐based CCN 0.4 and N a yielded a maximum ACI CCN = 0.88–0.92, a value slightly less than the ship‐based ACI CCN , but still consistent with aircraft‐based studies in the eastern Pacific.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".