Maritime low-level clouds are the source of great uncertainty in future climate predications (Bony & Dufresne, 2005; Bony et al., 2006). Despite numerous studies, these clouds remain poorly simulated in global models (e.g., Brunke et al., 2019;Klein et al., 2017). Lower-tropospheric stability (LTS) has a strong linear relationship with seasonal low cloud stratiform amount (Klein & Hartmann, 1993;Slingo, 1987;Wood & Bretherton, 2006). The relationship between LTS and stratiform cloud amount has been established observationally (Klein & Hartmann, 1993;Slingo, 1980) and theoretically using large-eddy simulations (Chung et al., 2012). This relationship was implemented in early versions of the Community Atmosphere Model (Collins et al., 2004). It was also used to parameterize low cloud amount, such as in previous versions of the Community Earth System Model in the determination of grid cell stratiform cloud amount (Neale et al., 2010). Wood and Bretherton (2006) established another strong correlation between stratiform cloud fraction and estimated inversion strength (EIS). EIS estimates the strength of the planetary boundary layer inversion from LTS by accounting for the change in lapse rate in the troposphere. Wood and Bretherton (2006) found EIS to be a better predictor of stratiform cloud fraction than LTS over both the subtropics and the midlatitudes. The relationships between EIS and low cloud amount have also been utilized in climate sensitivity studies to predict future changes in low cloud amount and cloud feedback (e.g.,
Clouds substantially impact the Earth system's radiative balance (Brunke et al., 2010). Their radiative impact on the Earth's energy balance is one of the largest uncertainties in the Earth system (Boucher et al., 2013). There has long been a large spread in simulated cloud feedbacks (Bony & Dufresne, 2005), resulting from a high uncertainty in the simulation of clouds in Earth system models (ESMs) (Bony et al., 2006). This uncertainty stems from a wide spread in the simulation of cloud characteristics like liquid and ice water paths (Kormurcu et al., 2014).Cloud simulation uncertainty also stems from the representation of droplet activation and ice nucleation (Choi et al., 2010(Choi et al., , 2014. Cloud droplets form around aerosols that have been activated called cloud condensation nuclei (CCN). This connection between aerosols and cloud formation is one critical aspect of aerosol-cloud interactions (ACI). There is the long recognized indirect effect of a decrease in cloud droplet radius owing to increased cloud droplet number concentration (at fixed liquid water content) with an increase in aerosol concentration. The increased cloud droplet number results in higher cloud albedo (Twomey, 1977) and decreased precipitation efficiency, which is speculated to increase cloud lifetime (Albrecht, 1989) as long as the air above-cloud is humid enough (Ackerman et al., 2004).Turbulent eddies strongly influence ACI and are critical to the maintenance of boundary layer clouds (Seinfeld et al., 2016). The strength of these eddies below clouds is characterized by turbulence kinetic energy (TKE), which determines updraft velocity at cloud base (Zhang et al., 2021). Updraft velocity controls the amount of
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