Submitted as: development and technical paper
09 Nov 2023
Submitted as: development and technical paper |  | 09 Nov 2023
Status: this preprint is currently under review for the journal GMD.

Subgrid-scale variability of cloud ice in the ICON-AES-1.3.00

Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval

Abstract. This paper presents a stochastical approach for the aggregation process rate in the ICON-GCM, which takes subgrid-scale variability into account. This method creates a stochastic parameterisation of the process rate by choosing a new specific cloud ice mass at random from a uniform distribution function. This distribution, which is consistent with the model's cloud cover scheme, is evaluated in terms of cloud ice mass variance with a combined satellite retrieval product (DARDAR) from the satellite cloud radar CloudSat and cloud lidar CALIPSO. For a realistic comparison with the simulated cloud ice, an estimate of precipitating and convective cloud ice is removed from the observational data set. The global patterns of simulated and observed cloud ice mixing ratio variance are in a good agreement, despite some regional differences. Due to this stochastical approach the yearly mean of cloud ice shows an overall decrease. As a result of the non-linear nature of the aggregation process, the yearly mean of the process rates increases when taking subgrid-scale variability into account. An increased process rate leads to a stronger transformation of cloud ice into snow and therefore, to a cloud ice loss. The yearly averaged global mean aggregation rate is more than 20 % higher at selected pressure levels due to the stochastical approach. A strong interaction of aggregation and accretion, however, lowers the effect of cloud ice loss due to a higher aggregation rate. The presented new stochastical method lowers the bias of the aggregation rate.

Sabine Doktorowski et al.

Status: open (until 04 Jan 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-34', Anonymous Referee #1, 06 Dec 2023 reply

Sabine Doktorowski et al.

Sabine Doktorowski et al.


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Short summary
Especially over the mid-latitudes precipiation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-GCM, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-GCM. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to a cloud ice loss and to an improvement of the process rate bias.