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Abstract Modeling experiments and field campaigns have evaluated shallow convective mixing as a potential constraint on the low‐cloud climate feedback, which is critical for establishing climate sensitivity. Yet the apparent relationship between low‐cloud fraction and shallow convective mixing differs substantially among general circulation models (GCMs), large eddy simulations, and both remote sensing and in situ observations. Here, we consider how changes in GCMs' representations of subgrid‐scale vertical moist fluxes can alter the cloud‐mixing relationship. Using vertical profiles of water vapor isotope ratios (δD) to characterize the strength of shallow convective mixing in a manner that can be compared directly to satellite observations, we evaluate the cloud‐mixing relationship produced in tiered experiments with the Community Atmosphere Model (CAM). From versions 5 to 6 of CAM, the most notable physics change is CLUBB, a scheme that unifies the representation of shallow convection and boundary layer turbulence through a joint probability density function (PDF) for subgrid velocity and moisture. CLUBB reduces the covariance between low‐cloud fraction and shallow convective mixing, producing a bivariate distribution that is more similar in character to monthly averaged satellite observations. Using parameter sensitivity experiments, we argue that CLUBB's ability to simulate skewness in the distribution of vertical velocity produces more isolated but stronger moist updrafts, which reduce the grid‐mean low‐cloud fraction while maintaining efficient hydrological connectivity between the boundary layer and the free troposphere. These results suggest that mixing is not an effective predictor of low‐cloud feedback in GCMs with PDF closure schemes. 

Abstract Describing the processes that regulate the flows and exchanges of water within the atmosphere and between the atmosphere and Earth’s surface is critical for understanding environmental change and predicting Earth’s future accurately. The heavy-to-light hydrogen and oxygen isotope ratios of water provide a useful lens through which to evaluate these processes due to their innate sensitivity to evaporation, condensation, and mixing. In this review, we examine how isotopic information advances our understanding about the origin and transport history of moisture in the atmosphere and about convective processes—including cloud mixing and detrainment, precipitation formation, and rain evaporation. Moreover, we discuss how isotopic data can be used to benchmark numerical simulations across a range of scales and improve predictive skill through data assimilation techniques. This synthesis of work illustrates that, when paired with air mass thermodynamic properties that are commonly measured and modeled (such as specific humidity and temperature), water’s isotope ratios help shed light on moist processes that help set the climate state.