Precipitation efficiency (PE) relates cloud condensation to precipitation and thus reflects how much of the total atmospheric condensate reaches the surface as precipitation. Because the PE in convective storms is directly linked to their updraft- and downdraft dynamics, it is a helpful metric to identify convective processes that influence precipitation. However, km-scale model simulations do not properly resolve convective processes such as individual updrafts and entrainment, which raises the question if such simulations can accurately represent PE. Here, we present two methods to derive PE from standard model output. The first method estimates PE from the state variables vertical velocity, temperature and pressure, whereas the second method estimates PE from ice water path (IWP) and precipitation. We validate the proposed methods with the explicitly calculated PE using a set of idealized Weather Research and Forecast model simulations of organized midlatitude convective storms at different horizontal grid spacings. We show that PE can be reliably estimated from state variables with an error of less than 5%, partly due to error cancellation effects. Additionally, PE can be simulated by km-scale models within ~15% accuracy compared to large-eddy simulations (LESs). The IWP-method is slightly less accurate with a stronger grid spacing dependency of the error, but since it is based on observable quantities, it allows for a validation of simulated PE with satellite observations. Finally, we analyze the grid spacing dependency of the climate change signal of PE and find that future decreases in PE in LESs are robustly captured by km-scale models.

Frozen cloud particles are an important link in the hydrological cycle and significantly influence the Earth’s energy budget. Despite their important role, observational records constraining concentrations of atmospheric ice remain severely limited. While combined radar and lidar estimates from the CloudSat and CALIPSO missions offer over a decade of high-quality data on ice hydrometeor concentrations, these estimates remain sparse. In contrast, products derived from passive satellite sensors typically provide better spatiotemporal coverage but disagree with CloudSat-baed measurements.   To address these limitations, we present a novel climate data record of total ice water path (TIWP), the Chalmers Cloud Ice Climatology (CCIC). It spans 40 years, from 1983 to the present, covering latitudes from 70 degree South to 70 degree North. CCIC offers TIWP estimates at three-hourly resolution from 1983 and half-hourly resolution from 2000 onwards. We demonstrate the long-term stability of CCIC by directly comparing it with CloudSat/CALIPSO-based estimates over the entire mission lifetime. Additionally, we assess CCIC against other long-term TIWP records, revealing that CCIC yields most accurate TIWP estimates compared to CloudSat/CALIPSO-based reference estimates. An investigation of the regional trends in TIWP shows good agreement between four observational datasets and ERA5 for the most recent 20 years. However, the consistency decreases for 40-year trends.   The CCIC climate record closes the gap between existing long-term TIWP records and CloudSat/CALIPSO-based reference measurements. The estimates’ continuous coverage and demonstrated accuracy make it a valuable resource for lifecycle studies of storms and the analysis of fine-scale cloud features in a changing climate.

Global kilometer-scale models are the future of Earth system models as they can explicitly simulate organized convective storms and their associated extreme weather. Here, we comprehensively examined tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non-hydrostatic domains) models for both summer and winter phases by applying eight different feature trackers to the simulations and satellite observations. Although different trackers produce substantial differences (a factor of 2-3) in observed MCS frequency and their contribution to total precipitation, model-observation differences in MCS statistics are more consistent among the trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi-model mean biases of 2.9% over land and -0.5% over ocean. However, most models underestimate the MCS precipitation amount (23%) and their contribution to total precipitation (17%) relative to observations. These biases show large inter-model variability, but are generally smaller over land (13%) than over ocean (21%) on average. MCS diurnal cycle and cloud shield characteristics are better simulated than precipitation. Most models overestimate MCS precipitation intensity and underestimate stratiform rain contribution (up to a factor of 2), particularly over land. Models also predict a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, and many models simulate a greater sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed in this work provide process-oriented diagnostics for future model development efforts.