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High-Resolution Multi-scale Modeling Frameworks (HR) -- global climate modelsthat embed separate, convection-resolving models with high enough resolution toresolve boundary layer eddies -- have exciting potential for investigating lowcloud feedback dynamics due to reduced parameterization and ability formultidecadal throughput on modern computing hardware. However low clouds inpast HR have suffered a stubborn problem of over-entrainment due to anuncontrolled source of mixing across the marine subtropical inversionmanifesting as stratocumulus dim biases in present-day climate, limiting theirscientific utility. We report new results showing that this over-entrainmentcan be partly offset by using hyperviscosity and cloud droplet sedimentation.Hyperviscosity damps small-scale momentum fluctuations associated with theformulation of the momentum solver of the embedded LES. By considering thesedimentation process adjacent to default one-moment microphysics in HR,condensed phase particles can be removed from the entrainment zone, whichfurther reduces entrainment efficiency. The result is an HR that is able toproduce more low clouds with a higher liquid water path and a reducedstratocumulus dim bias. Associated improvements in the explicitly simulatedsub-cloud eddy spectrum are observed. We report these sensitivities inmulti-week tests and then explore their operational potential alongsidemicrophysical retuning in decadal simulations at operational 1.5 degreeexterior resolution. The result is a new HR having desired improvements in thebaseline present-day low cloud climatology, and a reduced global mean bias androot mean squared error of absorbed shortwave radiation. We suggest it shouldbe promising for examining low cloud feedbacks with minimal approximation.

Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics