Implementation of the Stochastic Multicloud Model in the NCEP Climate Forecast System version 2 (CFSv2)

Thursday, 21 April 2016
Plaza Grand Ballroom (The Condado Hilton Plaza)
Bidyut B. Goswami, New York Univ., Abu Dhabi, United Arab Emirates; and R. Phani, B. Khouider, P. Mukhopadhyay, and A. J. Majda

We present here the implementation of the stochastic multicloud model (SMCM) (khouider et al 2010) in the NCEP Climate forecast system version 2 (CFSv2).

The final goal of this effort is to improve the Indian Summer Monsoon weather and climate through better-organized tropical convection in CFSv2. The fidelity of CFSv2 in simulating the mean state of the global climate, particularly the Indian summer monsoon, relative to the CMIP5 models (Sabeer et al 2013) is the reason behind choosing CFSv2 as the GCM to implement SMCM.

We expect to see an improved climate simulation in SMCM-CFSv2 because of the theoretically sound and tested design of the multicloud approach (Khouider and Majda 2006, and the relevant subsequent work thereafter).

In order to implement SMCM in CFSv2, first we identify different climatic regions based on the mean state of the global climate (using the CFSR 20year monthly climatology). Then we initialize the climatological values (computed from the CFSR 20year monthly climatology) of the variables required in the multicloud parameterization scheme, for the different climatic zones. We input moisture, temperature and PBL height from the CFSv2 to the multicloud parameterization module and then compute the corresponding variables that were initialized from the mean state. Then we compute the deviation of those variables from the background state. Based on middle troposphere dryness, we compute the heating rates for the deep, congestus and stratiform convection from these deviations from the background (deterministic approach). The stochastic extension involves the evolution of the cloud area fractions, associated to each one of the three cloud types, which are represented by a stochastic lattice subgrid model whose random transitions depend on CAPE and large-scale tropospheric dryness. The stochastic model feedback, to the GCM dynamics, occurs through the modulation of the heating rates by the cloud area fractions.

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