Articles | Volume 17, issue 7
Atmos. Chem. Phys., 17, 4731–4749, 2017
Atmos. Chem. Phys., 17, 4731–4749, 2017

Research article 11 Apr 2017

Research article | 11 Apr 2017

Direct comparisons of ice cloud macro- and microphysical properties simulated by the Community Atmosphere Model version 5 with HIPPO aircraft observations

Chenglai Wu1,2, Xiaohong Liu1, Minghui Diao3, Kai Zhang4, Andrew Gettelman5, Zheng Lu1, Joyce E. Penner6, and Zhaohui Lin2 Chenglai Wu et al.
  • 1Department of Atmospheric Science, University of Wyoming, Laramie, Wyoming, USA
  • 2International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • 3Department of Meteorology and Climate Science, San Jose State University, San Jose, California, USA
  • 4Pacific Northwest National Laboratory, Richland, Washington, USA
  • 5National Center for Atmospheric Research, Boulder, Colorado, USA
  • 6Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, Michigan, USA

Abstract. In this study we evaluate cloud properties simulated by the Community Atmosphere Model version 5 (CAM5) using in situ measurements from the HIAPER Pole-to-Pole Observations (HIPPO) campaign for the period of 2009 to 2011. The modeled wind and temperature are nudged towards reanalysis. Model results collocated with HIPPO flight tracks are directly compared with the observations, and model sensitivities to the representations of ice nucleation and growth are also examined. Generally, CAM5 is able to capture specific cloud systems in terms of vertical configuration and horizontal extension. In total, the model reproduces 79.8 % of observed cloud occurrences inside model grid boxes and even higher (94.3 %) for ice clouds (T ≤ −40 °C). The missing cloud occurrences in the model are primarily ascribed to the fact that the model cannot account for the high spatial variability of observed relative humidity (RH). Furthermore, model RH biases are mostly attributed to the discrepancies in water vapor, rather than temperature. At the micro-scale of ice clouds, the model captures the observed increase of ice crystal mean sizes with temperature, albeit with smaller sizes than the observations. The model underestimates the observed ice number concentration (Ni) and ice water content (IWC) for ice crystals larger than 75 µm in diameter. Modeled IWC and Ni are more sensitive to the threshold diameter for autoconversion of cloud ice to snow (Dcs), while simulated ice crystal mean size is more sensitive to ice nucleation parameterizations than to Dcs. Our results highlight the need for further improvements to the sub-grid RH variability and ice nucleation and growth in the model.

Short summary
This study utilizes a novel approach to directly compare the CAM5-simulated cloud macro- and microphysics with the collocated HIPPO observations for the period of 2009 to 2011. The model cannot capture the large spatial variabilities of observed RH, which is responsible for much of the model missing low-level warm clouds. A large portion of the RH bias results from the discrepancy in water vapor. The model underestimates the observed number concentration and ice water content.
Final-revised paper