Evaluation of the convective mass flux profiles associated with cumulus parameterization schemes of CMIP5 models

Abstract

While the numerical models are being run with increasing resolutions, the parameterization of cumulus convection used in the general circulation models, irrespective of closure assumption and trigger mechanism, continue to use the mass flux framework. To address one of the most important components of convective parameterization, vertical profile of mass flux is examined. We have compared the convective mass flux of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models during Boreal summer over the Eastern Pacific, Western Pacific and Indian Ocean with that of ERA – Year of Tropical Convection (YOTC) reanalysis dataset. The analyses suggest that most of the models overestimate the mass flux by an order over all the oceanic basins and interestingly the vertical structure also appears similar for all the CMIP5 models irrespective of ocean basins. In view of this, we state that the improper mass flux distribution in the cumulus parameterization schemes of global models need to be improved to reduce some of the uncertainties arising from the cumulus schemes of climate models which in turn impact the precipitation bias of the models.

This is a preview of subscription content, log in to check access.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

References

  1. Adler R F, Huffman G J, Chang A, Ferraro R, Xie P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P and Nelkin E 2003 The version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present); J. Hydrometeorol.4 1147–1167.

    Article  Google Scholar 

  2. Arakawa A and Schubert W H 1974 Interaction of a cumulus cloud ensemble with the large-scale environment. Part I; J. Atmos. Sci. 31(3) 674–701, https://doi.org/10.1175/1520-0469(1974)0312.0.CO;2.

    Article  Google Scholar 

  3. Bechtold P, Bazile E, Guichard F and Mascart P 2001 A mass-flux convection scheme for regional and global models; Quart. J. Roy. Meteorol. Soc. 127 869–886, https://doi.org/10.1256/smsqj.57308.

    Article  Google Scholar 

  4. Bougeault P 1985 A simple parameterization of the large scale effect of cumulus convection; Mon. Weather Rev.113 2108–2121.

    Article  Google Scholar 

  5. Dandi A R, Sabeerali C T, Chattopadhyay R, Rao D N, George G, Dhakate A, Salunke K, Srivastava A and Rao A S 2016 Indian summer monsoon rainfall simulation and prediction skill in the CFSv2 coupled model: Impact of atmospheric horizontal resolution; J. Geophys. Res. Atmos.121(5) 2205–2221.

    Article  Google Scholar 

  6. Donner L J 1993 A cumulus parameterization including mass fluxes, vertical momentum dynamics, and mesoscale effects; J. Atmos. Sci.50 889–906.

    Article  Google Scholar 

  7. Emanuel K A 1991 A scheme for representing cumulus convection in large-scale models; J. Atmos. Sci.48 2313–2329.

    Article  Google Scholar 

  8. Ganai M, Krishna R P M, Mukhopadhyay P and 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(16) 9301–9323.

    Article  Google Scholar 

  9. Gregory D and Rowntree P R 1990 A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure; Mon. Weather Rev.118 1483–1506.

    Article  Google Scholar 

  10. Han J and Pan H-L 2011 Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System; Wea. Forecasting26 520–533, https://doi.org/10.1175/WAF-D-10-05038.1.

    Article  Google Scholar 

  11. Huffman G J, Bolvin D T and Nelkin E J et al. 2007 The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales; J. Hydrometeorol. 8 38–55, https://doi.org/10.1175/JHM560.1.

    Article  Google Scholar 

  12. Kain J S and Fritsch J M 1990 A one-dimensional entraining/detraining Plume model and its application in convective parameterization; J. Atmos. Sci.47 2784–2802.

    Article  Google Scholar 

  13. Kumar V V, Jakob C and Protat A et al. 2015 Mass-flux characteristics of tropical cumulus clouds from wind profiler observations at Darwin, Australia; J. Atmos. Sci.72 1837–1855, https://doi.org/10.1175/JAS-D-14-0259.1.

    Article  Google Scholar 

  14. Kim H-M, Webster P J and Curry J A 2012 Seasonal prediction skill of ECMWF system 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter; Clim. Dyn.39 2957–2973, https://doi.org/10.1007/s00382-012-1364-6.

    Article  Google Scholar 

  15. Masunaga H and Luo Z J 2016 Convective and large-scale mass flux profiles over tropical oceans determined from synergistic analysis of a suite of satellite observations; J. Geophys. Res. 121 7958–7974, https://doi.org/10.1002/2016JD024753.

    Article  Google Scholar 

  16. Rienecker M M et al. 2011 MERRA: NASA’s modern-era retrospective analysis for research and applications; J. Clim. 24 3624–3648, https://doi.org/10.1175/JCLI-D-11-00015.1.

    Article  Google Scholar 

  17. Sperber K R, Annamalai H and Kang I S et al. 2013 The Asian summer monsoon: An intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century; Clim. Dyn.41.

  18. Taylor K E, Stouffer R J and Meehl G A 2012 An overview of CMIP5 and the experiment design; Bull. Am. Meteorol. Soc.93 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1.

    Article  Google Scholar 

  19. Yanai M, Esbensen S and Chu J 1973 Determination of the bulk properties of tropical cloud clusters from large heat and moisture budgets; J. Atmos. Sci.30 611–627.

    Article  Google Scholar 

Download references

Acknowledgements

Authors thank the Director, IITM, Pune for motivation and encouragement. IITM, Pune is fully funded by Ministry of Earth Sciences, Government of India, New Delhi. Authors gratefully acknowledge the comments of anonymous reviewers and editor which has helped to improve the manuscript. We would like to thank GSFC/DAAC, NASA for providing TRMM for (http://mirador.gsfc.nasa.gov/cgibin/mirador/presentNavigation.pl?tree=project&project=TRMM&dataGroup=Gridded), ESRL for GPCP (https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html) datasets, and MERRA for (http://disc.sci.gsfc.nasa.gov/daacbin/FTPSubset2.pl) datasets. We would also like to thank ESGF-CoG for providing CMIP5 dataset (https://esgf-node.llnl.gov/search/esgf-llnl/). We acknowledge ECMWF for providing ERA-YOTC datasets (https://apps.ecmwf.int/datasets/data/yotc-od/levtype=sfc/type=an/). First author (PM) gratefully acknowledges the discussion and guidance provided by Dr Zhengzhao Johnny Luo, Dept. of Earth & Atmospheric Sciences, City College, City University of New York and Dr H Masunaga, Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan. This paper is part of KR’s PhD thesis.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Parthasarathi Mukhopadhyay.

Additional information

Supplementary materials pertaining to this article are available on the Journal of Earth Science Website (http://www.ias.ac.in/Journals/Journal_of_Earth_System_Science).

Communicated by Kavirajan Rajendran

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 111 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mukhopadhyay, P., Roy, K. Evaluation of the convective mass flux profiles associated with cumulus parameterization schemes of CMIP5 models. J Earth Syst Sci 129, 138 (2020). https://doi.org/10.1007/s12040-020-01400-5

Download citation

Keywords

  • Convective mass flux
  • improper vertical distribution
  • CMIP5 models