Share Email Print

Proceedings Paper

Improvement of Systematic Bias of mean state and the intraseasonal variability of CFSv2 through superparameterization and revised cloud-convection-radiation parameterization
Author(s): P. Mukhopadhyay; R. Phani Murali Krishna; Bidyut B. Goswami; S. Abhik; Malay Ganai; M. Mahakur; Marat Khairoutdinov; Jimmy Dudhia
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Inspite of significant improvement in numerical model physics, resolution and numerics, the general circulation models (GCMs) find it difficult to simulate realistic seasonal and intraseasonal variabilities over global tropics and particularly over Indian summer monsoon (ISM) region. The bias is mainly attributed to the improper representation of physical processes. Among all the processes, the cloud and convective processes appear to play a major role in modulating model bias. In recent times, NCEP CFSv2 model is being adopted under Monsoon Mission for dynamical monsoon forecast over Indian region. The analyses of climate free run of CFSv2 in two resolutions namely at T126 and T382, show largely similar bias in simulating seasonal rainfall, in capturing the intraseasonal variability at different scales over the global tropics and also in capturing tropical waves. Thus, the biases of CFSv2 indicate a deficiency in model’s parameterization of cloud and convective processes. Keeping this in background and also for the need to improve the model fidelity, two approaches have been adopted. Firstly, in the superparameterization, 32 cloud resolving models each with a horizontal resolution of 4 km are embedded in each GCM (CFSv2) grid and the conventional sub-grid scale convective parameterization is deactivated. This is done to demonstrate the role of resolving cloud processes which otherwise remain unresolved. The superparameterized CFSv2 (SP-CFS) is developed on a coarser version T62. The model is integrated for six and half years in climate free run mode being initialised from 16 May 2008. The analyses reveal that SP-CFS simulates a significantly improved mean state as compared to default CFS. The systematic bias of lesser rainfall over Indian land mass, colder troposphere has substantially been improved. Most importantly the convectively coupled equatorial waves and the eastward propagating MJO has been found to be simulated with more fidelity in SP-CFS. The reason of such betterment in model mean state has been found to be due to the systematic improvement in moisture field, temperature profile and moist instability. The model also has better simulated the cloud and rainfall relation. This initiative demonstrates the role of cloud processes on the mean state of coupled GCM. As the superparameterization approach is computationally expensive, so in another approach, the conventional Simplified Arakawa Schubert (SAS) scheme is replaced by a revised SAS scheme (RSAS) and also the old and simplified cloud scheme of Zhao-Karr (1997) has been replaced by WSM6 in CFSV2 (hereafter CFS-CR). The primary objective of such modifications is to improve the distribution of convective rain in the model by using RSAS and the grid-scale or the large scale nonconvective rain by WSM6. The WSM6 computes the tendency of six class (water vapour, cloud water, ice, snow, graupel, rain water) hydrometeors at each of the model grid and contributes in the low, middle and high cloud fraction. By incorporating WSM6, for the first time in a global climate model, we are able to show a reasonable simulation of cloud ice and cloud liquid water distribution vertically and spatially as compared to Cloudsat observations. The CFS-CR has also showed improvement in simulating annual rainfall cycle and intraseasonal variability over the ISM region. These improvements in CFS-CR are likely to be associated with improvement of the convective and stratiform rainfall distribution in the model.

These initiatives clearly address a long standing issue of resolving the cloud processes in climate model and demonstrate that the improved cloud and convective process paramterizations can eventually reduce the systematic bias and improve the model fidelity.

Paper Details

Date Published: 3 May 2016
PDF: 6 pages
Proc. SPIE 9882, Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions VI, 98820Z (3 May 2016); doi: 10.1117/12.2222982
Show Author Affiliations
P. Mukhopadhyay, Indian Institute of Tropical Meteorology (India)
R. Phani Murali Krishna, Indian Institute of Tropical Meteorology (India)
Bidyut B. Goswami, Univ. of Victoria (Canada)
S. Abhik, Monash Univ. (Australia)
Bureau of Meteorology (Australia)
Malay Ganai, Indian Institute of Tropical Meteorology (India)
M. Mahakur, Indian Institute of Tropical Meteorology (India)
Marat Khairoutdinov, Stony Brook Univ. (United States)
Jimmy Dudhia, National Ctr. for Atmospheric Research (United States)

Published in SPIE Proceedings Vol. 9882:
Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions VI
Tiruvalam N. Krishnamurti; Madhavan Nair Rajeevan, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?