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Record W4392584908 · doi:10.5194/egusphere-egu24-5449

Secondary ice production within mixed-phase clouds in cold air outbreaks over the North Atlantic. 

2024· preprint· en· W4392584908 on OpenAlexaboutno aff
Michael Biggart, T. W. Choularton, M. W. Gallagher, Keith Bower, Gary Lloyd, Benjamin J. Murray

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersNatural Environment Research CouncilSight Research UK
KeywordsPhase (matter)Production (economics)Environmental scienceOceanographyOutbreakAtmospheric sciencesClimatologyMeteorologyGeographyGeologyChemistryBiologyEconomicsVirology

Abstract

fetched live from OpenAlex

In-cloud measurements of ice crystal concentrations often greatly exceed ice nucleating particle (INP) concentrations. This discrepancy can be accounted for by secondary ice production (SIP), describing mechanisms producing new ice crystals from the presence of existing primary ice particles. As ice particle formation in clouds strongly influences precipitation and earth’s radiative balance, accurate representation of SIP processes is critical for global climate and weather prediction model simulations. However, the dominant SIP mechanisms operating in different cloud systems remain poorly understood. This study aims to improve understanding of SIP within mixed-phase clouds associated with cold air outbreaks (CAOs). We examine in-situ ice particle and ice-nucleating particle measurements made in October-November 2022, using the UK FAAM BAE 146 research aircraft, during a set of CAOs in the North-western Atlantic over the Labrador Sea. This flight campaign comprised part of the M-PHASE project, part of the NERC-funded CloudSense programme, which aims to reduce uncertainties in climate sensitivity due to clouds. Detailed measurements of cloud microphysical properties were made to study the evolution of stratocumulus clouds as they advect southwards before breaking up under increasingly convective conditions.In-cloud ice crystal concentrations measured with 2D-S (size range 10 - 1280 μm) and HVPS (size range 150 μm - 19.2 mm) optical array probes frequently exceeded INP concentrations measured at the same temperature. Peak ice particle concentrations greater than 200 L-1 were recorded on numerous flights, several orders of magnitude above INP concentrations. These ice concentration enhancements were observed between -5 and -10 oC, within the active temperature range for the Hallett-Mossop SIP process. Analysis of corresponding ice particle imagery from the 2D-S and Cloud Particle Imager instruments shows that small hollow columns, often mixed with larger heavily rimed particles, were the dominant ice crystal habits, providing further evidence of rime splintering. A second ice concentration peak at around -17oC was also observed. Large irregularly shaped ice crystals were present during this period, suggesting that fragmentation due to ice-ice collisions may be another active SIP mechanism.We identify a series of SIP events across the flight campaign, with their short-lived nature suggesting ice multiplication is active across limited spatial extents. These segments of elevated ice concentrations are heavily populated by ice crystals of diameter < 100μm. Overall, SIP is observed to increase across convective regions of the CAO, with stratocumulus regions upwind often consisting mainly of supercooled water.This work provides critical information for numerical modelling studies requiring detailed representation of SIP processes within mixed-phase clouds across the transition region from stratocumulus to convective regimes in CAOs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.241
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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