Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00
-
Published:2024-04-19
Issue:8
Volume:17
Page:3099-3110
-
ISSN:1991-9603
-
Container-title:Geoscientific Model Development
-
language:en
-
Short-container-title:Geosci. Model Dev.
Author:
Doktorowski SabineORCID, Kretzschmar JanORCID, Quaas JohannesORCID, Salzmann MarcORCID, Sourdeval OdranORCID
Abstract
Abstract. This paper presents a stochastic approach for the aggregation process rate in the ICOsahedral Nonhydrostatic general circulation model (ICON-AES), which takes subgrid-scale variability into account. This method creates a stochastic parameterization 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 the Cloud–Aerosol Lidar and Infrared Pathfinder Observations (CALIPSO). The global patterns of simulated and observed cloud ice mixing ratio variance are in a good agreement, despite an underestimation in the tropical regions, especially at lower altitudes, and an overestimation in higher latitudes from the modeled variance. Due to this stochastic approach the yearly mean of cloud ice shows an overall decrease. As a result of the nonlinear 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 stochastic approach. A strong interaction of aggregation and accretion, however, lowers the effect of cloud ice loss due to a higher aggregation rate. The new stochastic method presented lowers the bias of the aggregation rate.
Funder
Deutsche Forschungsgemeinschaft
Publisher
Copernicus GmbH
Reference39 articles.
1. Bergeron, T.: On the physics of clouds and precipitation, Proc. 5th Assembly UGGI, Lisbon, Portugal, 156–180, https://worldcat.org/oclc/31921934 (last access: 16 April 2024), 1935. a 2. Berner, J., Achatz, U., Batté, L., Bengtsson, L., de la Cámara, A., Christensen, H. M., Colangeli, M., Coleman, D. R. B., Crommelin, D., Dolaptchiev, S. I., Franzke, C. L. E., Friederichs, P., Imkeller, P., Järvinen, H., Juricke, S., Kitsios, V., Lott, F., Lucarini, V., Mahajan, S.,Palmer, T. N., Penland, C., Sakradzija, M., von Storch, J.-S., Weisheimer, A., Weniger, M., and Williams, P. D.: Stochastic parameterization: Toward a new view of weather and climate models, B. Am. Math. Soc., 98, 565–588, https://doi.org/10.1175/BAMS-D-15-00268.1, 2017. a 3. Boutle, I. A., Abel, S. J., Hill, P. G., and Morcrette, C. J.: Spatial variability of liquid cloud and rain: observations and microphysical effects, Q. J. Roy. Meteor. Soc., 140, 583–594, https://doi.org/10.1002/qj.2140, 2014. a, b 4. Crueger, T., Giorgetta, M. A., Brokopf, R., Esch, M., Fiedler, S., Hohenegger, C., Kornblueh, L., Mauritsen, T., Nam, C., Naumann, A. K., Peters, K., Rast, S., Roeckner, E., Sakradzija, M., Schmidt, H., Vial, J., Vogel, R., and Stevens, B.: ICON-A, The Atmosphere Component of the ICON Earth System Model: II. Model Evaluation, J. Adv. Model Earth Sy., 10, 1638–1662, https://doi.org/10.1029/2017MS001233, 2018. a 5. Delanoë, J. and Hogan, R. J.: A variational scheme for retrieving ice cloud properties from combined radar, lidar, and infrared radiometer, J. Geophys. Res., 113, D07204, https://doi.org/10.1029/2007JD009000, 2008. a
|
|