Revised cloud and convective parameterization in CFSv2 improve the underlying processes for northward propagation of Intraseasonal oscillations as proposed by the observation-based study
The performance of revised climate forecast system version 2 (CFSv2) are evaluated on the simulation of the underlying cloud and convective processes associated with the strong and weak boreal summer intraseasonal oscillations (BSISOs) events. The revised version of the CFSv2 consists of a six-class Weather Research Forecasting single moment (WSM6) cloud microphysics scheme and the default version has Zhao and Carr (ZC) cloud microphysics scheme. Both the version uses revised simplified Arakawa-Schubert (RSAS) convection scheme. The study reveals that the revised version of CFSv2 (RSAS-WSM) is able to better simulate the northward propagation of BSISOs and associated dynamical and thermodynamical mechanism put forward by earlier observation-based studies. It is found that the large-scale organized northwest-southeast tilted structure of rain band is better captured in RSAS-WSM simulation as compared to the default version of CFSv2 (RSAS-ZC) during strong BSISO events. Further, the reasonable large-scale or stratiform rainfall associated with the northward propagating strong BSISO events is seen in RSAS-WSM while it is completely missing in RSAS-ZC simulation. The pressure-latitude profiles of cloud liquid water (CLW) and cloud ice (CLI) show more realistic steady northward propagation in RSAS-WSM simulation. Consistent with the CLW and CLI distribution and their influence on the large-scale heating structure, the large-scale condensation heating shows quasi-periodic northward propagation in RSAS-WSM whereas such type of distribution is not captured in RSAS-ZC simulation. The realistic representation of cloud processes in WSM leads to simulate reasonable dynamical and thermodynamical processes associated with the strong BSISO events which follows the observation-based hypothesis proposed by earlier studies.
IITM, Pune is fully funded by the Ministry of Earth Sciences (MoES), Government of India, New Delhi. Authors are grateful to the anonymous reviewers and editor for their constructive comments which have helped to improve the manuscript. We would like to thank NASA for providing TRMM data sets. All model runs are carried out on MoES “Aditya” High Performance Computing (HPC) system at IITM, Pune, India. The model simulation is archived at “Aditya” HPC and available on request from the corresponding author.
- Ganai M, Krishna RPM, Mukhopadhyay P, Mahakur M (2016) The impact of revised simplified Arakawa-Schubert scheme on the simulation of mean and diurnal variability associated with active and break phases of Indian Summer Monsoon using CFSv2. J Geophys Res Atmos 121:9301–9323. https://doi.org/10.1002/2016JD025393 CrossRefGoogle Scholar
- Griffies SM, Harrison MJ, Pacanowski RC, Rosati A (2004) A technical guide to MOM4, GFDL ocean group technical report 5,GFDL, pp 337Google Scholar
- Hong SY, Lim JOJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorol Soc 42:129–151Google Scholar
- Hong SY, Dudhia J,Chen SH (2004) A revised approach to ice microphysical processes for bulk parameterization of cloud and precipitation. Mon Weather Rev 132: 103–120. https://doi.org/10.1175/1520-0493(2004)132%3C0103:ARATIM%3E2.0.CO;2.CrossRefGoogle Scholar
- Lau WKM, Waliser DE (2005) Intraseasonal variability in the atmosphere–ocean climate system. Springer, Heidelberg, p 474Google Scholar
- Moorthi S, Pan HL, Caplan P (2001) Changes to the 2001 NCEP operational MRF/AVN global analysis/forecast system. NWS Tech Proc Bull 484:14Google Scholar
- Sharmila S, Pillai SA, Joseph S, Roxy M, Krishna RPM, Chattopadhyay R, Abhilash S, Sahai AK, Goswami BN (2013) Role of ocean-atmosphere interaction on northward propagation of Indian summer monsoon intra-seasonal oscillations (MISO). Clim Dyn 41:1651–1669. https://doi.org/10.1007/s00382-013-1854-1 CrossRefGoogle Scholar
- Sundqvist H, Berge E, Kristjansson JE (1989) Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. Mon Weather Rev 117: 1641–1657. https://doi.org/10.1175/1520-0493(1989)117%3C1641:CACPSW%3E2.0.CO;2.CrossRefGoogle Scholar
- Waliser DE, Jin K, Kang I-S, Stern WF, Schubert SD, Wu MLC, Lau K-M, Lee M-I, Krishnamurthy V, Kitoh A, Meehl GA, Galin VY, Satyan V, Mandke SK, Wu G, Liu Y, Park C-K (2003) AGCM simulations of intraseasonal variability associated with the Asian summer monsoon. Clim Dyn 21:423–446. https://doi.org/10.1007/s00382-003-0337-1 CrossRefGoogle Scholar