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Abstract A set of diagnostics based on simple, statistical relationships between precipitation and the thermodynamic environment in observations is implemented to assess phase 6 of the Coupled Model Intercomparison Project (CMIP6) model behavior with respect to precipitation. Observational data from the Atmospheric Radiation Measurement (ARM) permanent field observational sites are augmented with satellite observations of precipitation and temperature as an observational baseline. A robust relationship across observational datasets between column water vapor (CWV) and precipitation, in which conditionally averaged precipitation exhibits a sharp pickup at some critical CWV value, provides a useful convective onset diagnostic for climate model comparison. While a few models reproduce an appropriate precipitation pickup, most models begin their pickup at too low CWV and the increase in precipitation with increasing CWV is too weak. Convective transition statistics compiled in column relative humidity (CRH) partially compensate for model temperature biases—although imperfectly since the temperature dependence is more complex than that of column saturation. Significant errors remain in individual models and weak pickups are generally not improved. The conditional-average precipitation as a function of CRH can be decomposed into the product of the probability of raining and mean precipitation during raining times (conditional intensity). The pickup behavior is primarily dependent on the probability of raining near the transition and on the conditional intensity at higher CRH. Most models roughly capture the CRH dependence of these two factors. However, compensating biases often occur: model conditional intensity that is too low at a given CRH is compensated in part by excessive probability of precipitation. 

Convective transition statistics serve as diagnostics for the parameterization of convection in climate and weather forecast models by characterizing the dependence of convection on the humidity-temperature environment. The observed strong pickup of precipitation as a function of layer-averaged water vapor and temperature is captured in models with varying accuracy. For independent observational verification, a low-Earth orbiting satellite constellation of Global Navigation Satellite System (GNSS) polarimetric radio occultation (PRO) measurements would be spaced such that adjacent RO would capture different profiles within and immediately adjacent to convection. Here, the number of profile observations needed to distinguish between convective transition relations by different tropospheric temperature ranges is determined, over different tropical oceanic basins. To obtain these, orbit simulations were performed by flying different satellite constellations over global precipitation from the Global Precipitation Measurement (GPM) mission, varying the numbers of satellites, orbit altitude, and inclination. A 45-degree orbit inclination was found to be a good tradeoff between maximizing the number of observations collected per day, and the desired 50–150-km spacing between individual RO ray paths. Assuming a set of reasonable assumptions for net data yield, three tropospheric temperatures can be distinguished by 1 K with a six-month on-orbit duration from a constellation of at least three satellites. 

Abstract Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. We diagnose these biases in a cohort of nine CMIP6 models with subdaily output, assessing conditional instability in profiles of equivalent potential temperature,θ_e, and saturation equivalent potential temperature,θ_es, in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of theθ_esvertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and aθ_esmeasure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between largerθ_eslapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poorθ_e/θ_esstructure (those using variants of the Tiedtke scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic.