Reconciling Satellite–Model Discrepancies in Aerosol–Cloud Interactions Using Near-LES Simulations of Marine Boundary Layer Clouds
Bibliographic record
Abstract
Abstract. Aerosol–cloud interactions (ACI) remain the largest source of uncertainty in estimates of anthropogenic radiative forcing, largely due to inconsistent cloud liquid water path (LWP) responses to aerosol perturbations between observations and models. To reconcile this discrepancy, we conducted a series of simulations at near large-eddy scale (~200 m) driven by realistic meteorology over the Eastern North Atlantic, and evaluated LWP susceptibility, precipitation processes, and boundary layer thermodynamics using satellite and ground-based observations from the Atmospheric Radiation Measurement program. Simulated LWP responses show strong dependence on cloud state. Non-precipitating thin clouds exhibit a modest LWP decrease (mean susceptibility = −0.13) due to enhanced turbulent mixing and evaporation. In contrast, non-precipitating thick clouds show the largest model–observation mismatch, with simulated LWP susceptibilities significantly more positive than observed (+0.32 vs. −0.69). This discrepancy stems from excessive precipitation linked to underestimated entrainment, overactive accretion, and overly broad drop size distributions in polluted clouds. While our high-resolution setup avoids the excessive drizzling common in coarse models and captures key regime transitions, these biases persist—highlighting the need for improved representation of cloud-top processes, precipitation, and aerosol effects in numerical models, which cannot be fully resolved by increasing model resolution alone. Furthermore, misrepresented moisture inversions in reanalysis, which is used for the model initial and boundary conditions, introduce a moist bias in cloud-top relative humidity, amplifying positive LWP susceptibility. Our results also suggest that large negative cloud droplet number– LWP relationships in observations may reflect internal cloud processes rather than true ACI effects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".