Assessment of the cloud liquid water from climate models and reanalysis using satellite observations

  • Author(s): Jui-lin F. Li, Seungwon Lee, Hsi-Yen Ma, G. Stephens, and Bin Guan
  • DOI: 10.3319/TAO.2018.07.04.01
  • Keywords: GCM, LWC, CloudSat, CMIP5
  • Most GCMs exclude precipitating liquid
  • Caution is needed when using CloudSat’s liquid water content (LWC) data
  • CMIP5 LWC simulations show little or no improvement relative to CMIP3
Abstract

We perform a model-observation comparison and report on the state-of-the-art cloud liquid water content (CLWC) and path (CLWP) outputs from the present-day global climate models (GCMs) simulations in CMIP3/CMIP5, two other GCMs (UCLA and GEOS5) and two reanalyses (ECMWF Interim and MERRA) in comparison with two satellites observational datasets (CloudSat and MODIS). We use two different liquid water observation products from CloudSat and MODIS, for CLWP and their combined product for LWC with a method to remove the contribution from precipitating and convective core hydrometeors so that more meaningful model-observation comparisons can be made. Considering the CloudSat’s limitations of CLWC retrievals due to contamination from the precipitation and from radar clutter near the surface, an estimate CLWC is synergistically constructed using MODIS CLWP and CloudSat CLWC. The model-observation comparison shows that most of the CMIP3/CMIP5 annual mean CLWP values are overestimated by factors of 2-10 compared to observations globally. There are a number of CMIP5 models, including CSIRO, MPI and the UCLA GCM that perform well compared to the other models. For the vertical structure of CLWC, significant systematic biases are found with many models biased significantly high above the mid-troposphere. In the tropics, systematic high biases occur at all levels above 700 hPa. Based on the Taylor diagram, the ensemble performance of CMIP5 CLWP simulation shows little or no improvement relative to CMIP3.

 

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