EGU2020-4067
https://doi.org/10.5194/egusphere-egu2020-4067
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Evaluation of Marine Boundary layer cloud in the NCEP Climate Forecast System (Version 2) via Stochastic Multicloud Model

Kumar Roy1,2, Parthasarathi Mukhopadhyay1, Ravuri Phani Murali Krishna1, Bidyut Bikhash Goswami3, and Boualem Khouider3
Kumar Roy et al.
  • 1Indian Institute of Tropical Meteorology, Parameterization and Analyses Group, India
  • 2Department of Meteorology and Oceanography, College of Science and Technology, Andhra University, India
  • 3Department of Mathematics and Statistics, University of Victoria, BC, Canada

Marine boundary layer (MBL) cloud is one of the major sources of uncertainty in the climate models and they have been identified in the Intergovernmental Panel on Climate Change’s (IPCC’s) fourth assessment as a primary source of uncertainty in determining the sensitivity of climate models. Further simulating it realistically is a huge challenge. To better represent organized convection in the Climate Forecast System version 2 (CFSv2), a stochastic multicloud model (SMCM) parameterization is adopted and it has showed promising improvement in different features of tropical convection. But the simulation of marine boundary cloud in CFSv2 SMCM (EXP1) is yet to be ascertained. We have calibrated the model by using radar observations and followed Markov-chain process to generate key parameters like transition probability, required for EXP1. This paper describes climate simulations of the EXP1 and 25 year run is made and last 20years are analysed. It replaces pre-existing convection scheme (CTL) and shows improvement in many aspects of climate compared to CTL. In addition, global distribution of MBL cloud is also improved and it is also with better agreement with observational analysis, which is inaccurate in CTL. Further, the transition from stratocumulus to trade cumulus is well simulated in EXP1. These results are also supported also by quantitative analyses like Root Mean Square Error (RMSE) etc. The improvement seen in EXP1 can be largely attributed to the general improved in the representation of shallow and cumulus clouds compared to CTL.

How to cite: Roy, K., Mukhopadhyay, P., Krishna, R. P. M., Goswami, B. B., and Khouider, B.: Evaluation of Marine Boundary layer cloud in the NCEP Climate Forecast System (Version 2) via Stochastic Multicloud Model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4067, https://doi.org/10.5194/egusphere-egu2020-4067, 2020