A review of the progression of cloud physics from a subdiscipline of meteorology into the global science it is today is described. The discussion briefly touches on the important post‐war contributions of three key individuals who were instrumental in developing cloud physics into a global science. These contributions came on the heels of the post‐war weather modification efforts that influenced much of the early development of cloud physics. The review is centred on the properties of warm clouds primarily to limit the scope of the article and the connection between the early contributions to cloud physics and the current vexing problem of aerosol effects on cloud albedo is underlined. Progress toward estimating cloud properties from space and insights on warm cloud processes are described. Measurements of selected cloud properties, such as cloud liquid water path are now mature enough that multi‐decadal time series of these properties exist and this climatology is used to compare to analogous low‐cloud properties taken from global climate models. The too‐wet (and thus too bright) and the too‐dreary biases of models are called out underscoring the challenges we still face in representing warm clouds in Earth system models. We also provide strategies for using observations to constrain the indirect radiative forcing of the climate system.

This is the second part of the description and validation of the coupling of a warm‐rain microphysics scheme to a stochastic multi‐plume eddy‐diffusivity/mass‐flux parameterization. Part 1 provides the model description and initial validation of the parameterization in a single column model (SCM) against large eddy simulations. This manuscript provides additional comparisons against satellite observations of 500 unique simulation cases, an analysis of model parameter‐based sensitivities in those 500 cases, and an evaluation of the idealized LES case study paradigm for parameterization development. The SCM is forced by a weather reanalysis in the subtropical northeast Pacific region characterized by the stratocumulus‐to‐cumulus cloud transition. 18 scalar outputs are examined to identify the sensitivities to 22 of the model's parameters. The model is highly sensitive to parameters that determine the behavior of convective plumes, which collectively lead to the most sensitivity in 13 of the 18 outputs. Only a few parameters contribute sensitivities that are not highly correlated with those of other parameters. While forcing the model with two canonical field campaign cases results in similar picture of which parameters lead to high sensitivity in model outputs, the sensitivity magnitudes can be overestimated compared to when the model is forced by weather reanalyzes.

The finite resolutions of weather and climate models fundamentally constrain the spatial and temporal scales of processes that can be resolved by these models. Atmospheric dynamical and thermodynamical processes are highly non-linear and interactive on a wide range of spatial and temporal scales. Therefore, the impact of unresolved processes on the resolved scale flow must be parameterized (e.g., Pielke, 2002). The parameterization problem would be greatly simplified if the subgrid scale joint-probability-density-function (JPDF) of the model's thermodynamic and kinematic variables were known because most of the parameterized quantities could be directly linked to this JPDF. In many atmospheric models, multiple parameterizations are used in parallel to represent either specific parts of the atmosphere or aspects of an unresolved processes (e.g.,

Warming commonly causes high clouds to rise in cloud-resolving models (e.g., Kuang & Hartmann, 2007) and Global Climate Models (e.g., Soden & Vecchi, 2011). All else being fixed, increased cloud top height amplifies warming by suppressing changes in cloud top longwave radiative cooling to space. Furthermore, clouds respond to the detailed spatial structure of temperature and dynamical changes (Andrews et al., 2015;Su et al., 2017;Zhou et al., 2016), so responses to internal variability, such as the El Nino Southern Oscillation (ENSO), and longterm climate change may differ. It is desirable to distinguish between such changes to comprehensively evaluate observed cloud-height changes and model performance. To that end, here we use the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on NASA's Terra and Aqua satellites to simultaneously diagnose the linear trend and El Nino Southern Oscillation (ENSO)-correlated response of high-altitude cloud heights.The canonical fixed anvil temperature (FAT, Hartmann & Larson, 2002) hypothesis is that the altitude of maximum radiative cooling by water vapor in clear-sky conditions rises under warming as governed by the Clausius-Clapeyron relation, thereby driving anvil outflow at those levels (Boucher et al., 2013). However, full-physics climate models (Zelinka & Hartmann, 2010) and cloud-resolving models (Seeley et al., 2019) support a Proportionally Higher Anvil Temperature (PHAT) behavior where anvil clouds warm, albeit by less than the surface. Observational evidence links a warmer surface to higher cloud altitudes, although this is often limited to interannual variation rather than trends (e.g., Igel et al., 2014).High-cloud feedback also depends on changes in cloud fraction (CF) and optical depth. Multiple factors can change simultaneously, for example, some models show increased altitude and reduced fraction of tropical high clouds under warming (Bony et al., 2016;Cronin & Wing, 2017). A long-running hypothesis has been a

<p>Aerosol-cloud-interactions remain a large climate uncertainty; especially uncertain is the liquid water path (LWP) adjustment to varying aerosol concentrations in stratocumulus (StCu). Large-eddy simulations (LES) have found that the LWP response of StCu to aerosol can be either positive or negative depending on the cloud regime, whereas climate models simulate uniformly positive correlations. Observations of the real-world evolution of LWP in varying aerosol environments are needed to resolve the nature of the correlation between LWP and aerosol. We address this by analyzing a large ensemble of parcel trajectories over the southeast Pacific within the GOES-16 field of regard. Preliminary results are consistent with LES, showing consistent regime dependent evolution of the LWP depending on the initial cloud state, with LWP generally decreasing with varying number concentrations (N). However, LWP generally increases at low N, potentially supporting LES conclusions showing a cloud-regime dependence. To investigate this further, we will further condition observations by MERRA-2 large-scale environmental variables (e.g. estimated inversion strength, moisture, and boundary-layer decoupling). We expect environmental differences will correlate with the differences between changes in LWP at low and high N.</p>