Cloud ice heterogenity, its parameterization in a climate model and its impact on microphysical processes

Abstract

A correct representation of clouds and their microphysical processes in global climate models is still one of the biggest challenge. Especially the representation of the ice phase in clouds is crucial, since most of the global precipitation is formed via ice related processes. Since global climate models run on a very coarse resolution, the processes can not be resolved explicitly. The representation of cloud-ice related processes are parametrized based on the grid-box averaged cloud ice. Some of these parametrization are non linear, which causes biases. Given that cloud ice is distributed within such a large grid box (subgrid-scale variability), accounting for this variability helps reduce the bias in these non-linear processes. The only non-linear cloud-ice related process in the ICOsahedral Nonhydro-static Atmospheric general circulation model (ICON-AES) is the aggregation process, which describes the initial snow formation process in the model. A stochastic approach for taking subgrid-scale variability of cloud ice into account is implemented in the ag- gregation parametrization based on the cloud cover scheme, which is used in the ICON-AES. This stochastic approach describes a specific cloud ice mass randomly chosen from the derived distribution. Compared to a full numerical integration, this method does not need additional computational time. To get an idea on how aggregation changes effect the models microphysics a sensitivity study around some tunable parameters of the aggregation is created. A higher aggregation rate results in an overall cloud ice loss. Another process, which is strongly linked to the aggregation is the accretion of cloud ice, which describes the collection of cloud ice from snow. This process shows a reduction to an increase of aggregation. In order to evaluate the model simulations the derived cloud ice results are compared to satellite products. The chosen cloud ice distribution is evaluated by calculating the cloud ice variance in the model and with the help of the DARDAR (raDAR/liDAR) satellite data set, which combines measurements from the satellite cloud radar CloudSat and the Cloud–Aerosol Lidar and Infrared Pathfinder Observations (CALIPSO). Since the most of the global climate models distinguish between cloud ice and snow and provide an output for only stratiform clouds, the observations are adjusted to create comparable cloud ice masses. Additionally, the satellite simulator COSP is implemented in the ICON-AES to provide an alternative method for observations and simulations compar- isons. A passive instrument out form the Moderate-resolution Imaging Spectroradiometer (MODIS) satellite is taken to compare the simulated ice water path (IWP) results derived from the COSP-MODIS simulator. The overall comparison of DARDAR, MODIS, ICON-COSP-MODIS and ICON-AES IWP comparison show, an overestimation of the simulated cloud ice compared to observations, but also a difference depending on the used method for calculation. The comparison of the calculated cloud ice variance from ICON-AES and observations show a good agreement of the general pattern, but with some regional differences. The stochastic aggregation approach results in an aggregation rate increase and in a cloud ice loss, which allows a improvement of the simulated cloud ice in comparison to the satellites. The strong interaction between aggregation and accretion, however, reduces the impact of cloud ice loss. With the help of this stochastic approach, a decrease in the bias of the process rate is demonstrated. The warm rain fraction, which measures the amount of rain produced through the ice phase, is calculated using the radar simulator output from the COSP simulator. Models tend to underestimate the amount of rain generated via the ice phase. the new stochastic aggregation scheme in combination with the sensitivity studies show the model show a even larger underestimation, if the aggregation is increased. This result contradicts the previous assumption that increased snow production leads to a lower warm rain fraction. The derived cloud ice distribution is additional implemented in the subcolumn system of COSP. This distribution is used for snow and cloud ice for each cloudy subcolumn. It shows a reduction of the subcolumn averaged radar reflectivity, with a stronger change for distributed snow. For both experiments, the warm rain fraction increases. The MODIS simulator results does not show any significant changes