Ice production in northern hemisphere cold air-outbreak clouds: two contrasting aircraft campaigns
Bibliographic record
Abstract
Cold-air outbreaks (CAOs) are common high-impact weather events that produce extensive boundary layer clouds that have a substantial influence on our planet’s climate. These clouds are often supercooled and therefore their properties are susceptible to the formation of ice.  The amount of ice in these clouds has been identified as being particularly important for defining the magnitude of the cloud-climate feedback and climate sensitivity.To address ice production in northern hemisphere CAOs we conducted two contrasting aircraft campaigns in 2022.  One campaign (ACAO, 11 flights) was in March in the Norwegian and Barents Sea where cold air flowed from the ice-covered Arctic Ocean. The other (M-Phase, 12 flights) was in October-November and focused on the Labrador Sea with air coming from the Arctic Archipelago. In both campaigns, we used similar instruments on the FAAM BAe-146 research aircraft designed to probe the aerosol properties, cloud microphysics and atmospheric thermodynamics of the CAO events.  Flight sorties were designed to study aerosol-cloud interactions as the CAO developed through the stratus and into the cumulus regime.We found that INP concentrations in these Northern Hemisphere CAOs were orders of magnitude greater than CAO events over the Southern Ocean.  The springtime ACAO cases had systematically greater INP (and aerosol) concentrations than the autumnal Labrador Sea M-Phase cases. The presence of substantial amounts of mineral dust in the springtime Arctic, despite all local sources being covered in ice and snow, implies a reservoir of old INPs and aerosol in the springtime Arctic that originated from the low latitudes. This is supported by our global aerosol model. Primary ice production by INPs is shown to define the ice concentrations in the stratus regime in many cases, but in the cumulus regime there are pockets of very high ice concentrations that are indicative of secondary ice production.Our modelling work has demonstrated that INPs are key to defining the stratus to cumulus transition and the cases are providing an excellent test for the high-resolution regional modelling with the Met Office Unified Model.  We are also using ACAO cases to study how INPs interact with clouds in CAOs, where warm temperature INPs are preferentially lost through nucleation scavenging. Furthermore, we envisage that the data from these campaigns will provide a valuable resource for model development, hypothesis testing and contrasting with other CAO campaigns in other places and times. Given the stark contrast of primary ice production in CAO clouds in different locations and times around the globe, we conclude that the primary production of ice in model CAO clouds should be linked to the aerosol properties and knowledge of the local INP population to reduce uncertainty in cloud feedback and climate sensitivity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".