Ice production in northern hemisphere cold air-outbreak clouds: two contrasting aircraft campaigns
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».