English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Large cloud condensation nuclei (CCN) (e.g., aged dust particles and seasalt)cannot attain their equilibrium size during the typical timescale of clouddroplet activation. Cloud activation parameterizations applied to aerosol with alarge fraction of large CCN often do not account for this limitationadequately and can give biased predictions of cloud droplet numberconcentration (CDNC). Here we present a simple approach to address thisproblem that can easily be incorporated into cloud activationparameterizations. This method is demonstrated with activationparameterizations based on the "population splitting" concept of Nenes andSeinfeld (2003); it is shown that accounting for large CCN effectseliminates a positive bias in CDNC where the aerosol dry geometric diameteris greater than 0.5 μm. The method proposed here can also be extendedto include the water vapor depletion from pre-existing droplets and icecrystals in global and regional atmospheric models

Comprehensively accounting for the effect of giant CCN in cloud activation parameterizations