A new release of the Max Planck Institute for Meteorology Earth System Model version 1.2 (MPI‐ESM1.2) is presented. The development focused on correcting errors in and improving the physical processes representation, as well as improving the computational performance, versatility, and overall user friendliness. In addition to new radiation and aerosol parameterizations of the atmosphere, several relatively large, but partly compensating, coding errors in the model's cloud, convection, and turbulence parameterizations were corrected. The representation of land processes was refined by introducing a multilayer soil hydrology scheme, extending the land biogeochemistry to include the nitrogen cycle, replacing the soil and litter decomposition model and improving the representation of wildfires. The ocean biogeochemistry now represents cyanobacteria prognostically in order to capture the response of nitrogen fixation to changing climate conditions and further includes improved detritus settling and numerous other refinements. As something new, in addition to limiting drift and minimizing certain biases, the instrumental record warming was explicitly taken into account during the tuning process. To this end, a very high climate sensitivity of around 7 K caused by low‐level clouds in the tropics as found in an intermediate model version was addressed, as it was not deemed possible to match observed warming otherwise. As a result, the model has a climate sensitivity to a doubling of CO2 over preindustrial conditions of 2.77 K, maintaining the previously identified highly nonlinear global mean response to increasing CO2 forcing, which nonetheless can be represented by a simple two‐layer model.

The Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative‐convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud‐resolving models (CRMs), large eddy simulations (LES), and global cloud‐resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self‐aggregation in large domains and agree that self‐aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self‐aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations.

Moist convection is a key aspect of the extratropical summer climate and strongly affects the delicate balance of processes that determines the surface climate in response to larger-scale forcings. Previous studies using parameterized convection have found that the feedback between soil moisture and precipitation is predominantly positive (more precipitation over wet soils) over Europe. Here this feedback is investigated for one full month (July 2006) over the Alpine region using two different model configurations. The first one employs regional climate simulations performed with the Consortium for Small-Scale Modeling Model in Climate Mode (CCLM) on a grid spacing of 25 km. The second one uses the same model but integrated on a cloud-resolving grid of 2.2 km, allowing an explicit treatment of convection. Each configuration comprises one control and two sensitivity experiments. The latter start from perturbed soil moisture initial conditions. Comparison of the simulated soil moisture–precipitation feedback reveals significant differences between the two systems. The 25-km simulations sustain a strong positive feedback, while those at 2.2-km resolution are associated with a predominantly negative feedback. Thus the two systems yield not only different strengths of this key feedback but also different signs. This has important implications, with the cloud-resolving model exhibiting a shorter soil moisture memory and a smaller soil moisture–temperature feedback. Analysis shows that the different feedback signs relate to the sensitivity of the simulated convective development to the presence of a stable layer sitting on top of the planetary boundary layer. In the 2.2-km integrations, dry initial soil moisture conditions yield more vigorous thermals (owing to stronger daytime heating), which can more easily break through the stable air barrier, thereby leading to deep convection and ultimately to a negative soil moisture–precipitation feedback loop. In the 25-km integrations, deep convection is much less sensitive to the stable layer because of the design of the employed convective parameterization. The authors also show that there are considerable differences in the simulated soil moisture–precipitation feedback between low-resolution modeling frameworks using different cloud convection schemes.

Entrainment and detrainment processes have been recognised for a long time as key processes for cumulus convection and have recently witnessed a regrowth of interest mainly due to the capability of large-eddy simulations (LES) to diagnose these processes in more detail. This article has a twofold purpose. Firstly, it provides a historical overview of the past research on these mixing processes, and secondly, it highlights more recent important developments. These include both fundamental process studies using LES aiming to improve our understanding of the mixing process, but also more practical studies targeted toward an improved parametrised representation of entrainment and detrainment in large-scale models. A highlight of the fundamental studies resolves a long-lasting controversy by showing that lateral entrainment is the dominant mixing mechanism in comparison with the cloud-top entrainment in shallow cumulus convection. The more practical studies provide a wide variety of new parametrisations with sometimes conflicting approaches to the way in which the effect of the free tropospheric humidity on the lateral mixing is taken into account. An important new insight that will be highlighted is that, despite the focus in the literature on entrainment, it appears that it is rather the detrainment process that determines the vertical structure of the convection in general and the mass flux especially. Finally, in order to speed up progress and stimulate convergence in future parametrisations, stronger and more systematic use of LES is advocated.

ICON‐A is the new icosahedral nonhydrostatic (ICON) atmospheric general circulation model in a configuration using the Max Planck Institute physics package, which originates from the ECHAM6 general circulation model, and has been adapted to account for the changed dynamical core framework. The coupling scheme between dynamics and physics employs a sequential updating by dynamics and physics, and a fixed sequence of the physical processes similar to ECHAM6. To allow a meaningful initial comparison between ICON‐A and the established ECHAM6‐LR model, a setup with similar, low resolution in terms of number of grid points and levels is chosen. The ICON‐A model is tuned on the base of the Atmospheric Model Intercomparison Project (AMIP) experiment aiming primarily at a well balanced top‐of atmosphere energy budget to make the model suitable for coupled climate and Earth system modeling. The tuning addresses first the moisture and cloud distribution to achieve the top‐of‐atmosphere energy balance, followed by the tuning of the parameterized dynamic drag aiming at reduced wind errors in the troposphere. The resulting version of ICON‐A has overall biases, which are comparable to those of ECHAM6. Problematic specific biases remain in the vertical distribution of clouds and in the stratospheric circulation, where the winter vortices are too weak. Biases in precipitable water and tropospheric temperature are, however, reduced compared to the ECHAM6. ICON‐A will serve as the basis of further development and as the atmosphere component to the coupled model, ICON‐Earth system model (ESM).