Advertisement

Pure and Applied Geophysics

, Volume 176, Issue 8, pp 3697–3715 | Cite as

Evaluation of Indian Summer Monsoon Rainfall Using the NCEP Global Model: An SST Impact Study

  • Ancy ThomasEmail author
Article
  • 53 Downloads

Abstract

The study evaluates the Indian summer monsoon prediction skill of the Atmospheric General Circulation Model (AGCM) and the impact of sea surface temperature (SST) boundary forcing on the model performance. The National Center for Environmental Prediction's (NCEP's) T170/L42 AGCM model configured with a horizontal resolution of 75 × 75 km, with 42 vertical levels is used for the study. The SST-rainfall relationship is examined in the coupled Climate Forecast System version 2 (CFSv2) model, as CFSv2-predicted SST is used as input for the T170 model. The NCEP Global Forecast System-T170 (GFS-T170) simulations are carried out with boundary forcing of observed SST, CFSv2-predicted SST and the bias-corrected CFSv2 SST. An ensemble of seasonal runs was made using the initial conditions of May to September, and integrated up to September 30th. The significance of discontinuity in the initial conditions due to climate forecast system reanalysis (CFSR) is assessed based on the two-period approach of climatology for the two time scales of 1985–1998 and 1998–2009. CFSv2 predicted climatological summer monsoon rainfall with a significant dry bias over the three convection zones; Western Ghats, Central India and North-east India, and cold bias over the Indian ocean basin and central equatorial Pacific, with strong cold bias over a narrow region of equatorial Pacific. The model could capture 64% (16 out of 25) of the year’s rainfall anomaly signal. The skill of the model is improved in the recent period (1999–2009). The model could simulate the negative Nino 3 and excess rainfall and the La Nina event realistically for the year 1988. The model shows a large difference in Nino indices for the years 1987 and 1998, which led to the unrealistic rainfall simulation. The model has a low skill for indicating the relationship between the Indian Ocean Dipole (IOD) and Indian summer monsoon rainfall (ISMR). The CFSv2 model could not capture the strong positive correlation of the IOD and strong negative correlation of Nino 3 with the ISMR for the period 1999–2009 realistically, suggesting improvement of SST simulation in the CFSv2 model. The T170 model forced with observed SST shows wet bias in peninsular India and dry bias over North-east India, whereas that of CFSv2-predicted SST simulated a wet bias in peninsular India and widespread dry bias in North and Central India. When the model was forced with bias-corrected CFSv2 SST, the dry bias improved in North and Central India, and the intensity of wet bias increased in peninsular India. The model could capture 56, 48 and 64% of the year’s rainfall anomaly signal (positive or negative) correctly in the same sign for being forced with observed SST, CFSv2-predicted SST, and bias-corrected CFSv2 SST, respectively.

Keywords

Indian summer monsoon rainfall sea surface temperature climatology inter-annual variability 

Notes

Acknowledgements

The GFS-T170 model used in this work is developed by NCEP and the datasets used for the study are provided by NCEP–NCAR and IMD. Sincere thanks are due for their efforts for enhancement in atmospheric science research by providing this model and datasets online. The late D. R. Sikka was very much involved in this study and the author has very much benefited from his constant encouragement and wonderful ideas. The author thanks colleagues at HPC-Scientific & Engineering Applications Group, C-DAC for their help and support during this work. The author is grateful to Dr. Preethi Bhaskar, IITM for all the useful discussions and suggestions regarding the manuscript. The help and suggestions received from Dr. Basanta Kumar Samala are gratefully acknowledged. The author acknowledge the management of C-DAC for the use of the Param Padma computer for the simulations carried out for this work.

References

  1. Abhilash, S., Sahai, A. K., Borah, N., Chattopadhyay, R., Joseph, S., Sharmila, S., et al. (2014). Does bias correction in the forecasted SST improve the extended range prediction skill of active-break spells of Indian summer monsoon rainfall? Atmospheric Science Letters, 15, 114–119.  https://doi.org/10.1002/asl2.477.CrossRefGoogle Scholar
  2. Abhilash, S., Sahai, A. K., Pattnaik, S., Goswami, B. N., & Kumar, A. (2013). Extended range prediction of active-break spells of Indian summer monsoon rainfall using an ensemble prediction system in NCEP climate forecast system. International Journal of Climatology.  https://doi.org/10.1002/joc.3668.Google Scholar
  3. Azad, S., & Rajeevan, M. (2016). Possible shift in the ENSO-Indian monsoon rainfall relationship under future global warming. Scientific Reports, 6, 20145.  https://doi.org/10.1038/srep20145.CrossRefGoogle Scholar
  4. Bollasina, M. A., & Nigam, S. (2009). Indian Ocean SST, evaporation and precipitation during the South Asian summer monsoon in IPCC-AR4 coupled simulations. Climate Dynamics, 33, 1017–1032.CrossRefGoogle Scholar
  5. Chattopadhyay, R., Rao, S. A., Sabeerali, C. T., George, G., Rao Nagarjuna, D., Dhakate, A., et al. (2016). Large-scale teleconnection patterns of Indian summer monsoon as revealed by CFSv2 retrospective seasonal forecast runs. International Journal of Climatology, 36, 3297–3313.  https://doi.org/10.1002/joc.4556.CrossRefGoogle Scholar
  6. Chaudhari, H. S., Pokhrel, S., Saha, S. K., Dhakate, A., Yadav, R. K., Salunke, K., et al. (2013). Model biases in long coupled runs of NCEP CFS in the context of Indian summer monsoon. International Journal of Climatology, 33, 2013.  https://doi.org/10.1002/joc.3489,1057-1069.CrossRefGoogle Scholar
  7. Clough, S. A., Shephard, M. W., Mlawer, E. J., Delamere, J. S., Iacono, M. J., Cady-Pereira, K., et al. (2005). Atmospheric radiative transfer modeling: A summary of the AER codes. Journal of Quantitative Spectroscopy & Radiative Transfer, 91, 233–244.CrossRefGoogle Scholar
  8. Drbohlav, H.-K. L., & Krishnamurthy, V. (2010). Spatial structure, forecast errors, and predictability of the South Asian Monsoon in CFS monthly retrospective forecasts. Journal of Climate, 23, 4750–4769.  https://doi.org/10.1175/2010JCLI2356.1.CrossRefGoogle Scholar
  9. Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., et al. (2003). Implementation of Noah land surface model advances in the National Centers for environmental prediction operational mesoscale Eta model. Journal of Geophysical Research, 1089(D22), 8851.  https://doi.org/10.1029/2002JD003296.Google Scholar
  10. Gadgil, S. (2003). The Indian monsoon and its variability. Annual Review of Earth and Planetary Sciences, 31, 429–467.CrossRefGoogle Scholar
  11. Gadgil, S., Rajeevan, M., & Nanjundiah, R. S. (2005). Monsoon prediction—why yet another failure? Current Science, 88, 1389–1400.Google Scholar
  12. Gadgil, G., & Sajini, S. (1998). Monsoon precipitation in the AMIP runs. Climate Dynamics, 14, 659–689.CrossRefGoogle Scholar
  13. Ganai, M., Mukhopadhyaya, P., Krishna, R. P., & Mahakur, M. (2015). Impact of revised simplified Arakawa-Schubert convection parameterization scheme in CFSv2 on the simulation of the Indian summer monsoon. Climate Dynamics, 45, 881–902.  https://doi.org/10.1007/s00382-014-2320-4.CrossRefGoogle Scholar
  14. George, G., Rao, D. N., Sabeerali, C. T., Srivastava, A., & Rao, S. A. (2016). Indian summer monsoon prediction and simulation in CFSv2 coupled model. Atmospheric Science Letters, 17, 57–64.  https://doi.org/10.1002/asl.599.CrossRefGoogle Scholar
  15. Goswami, B. B., Deshpande, M. S., Mukhopadhyay, P., Saha, Subodh K., Rao, Suryachandra A., Murthugudde, R., et al. (2014a). Simulation of monsoon intraseasonal variability in NCEP CFSv2 and its role on systematic bias. Climate Dynamics, 43, 2014.  https://doi.org/10.1007/s00382-014-2089-5,2725-2745.CrossRefGoogle Scholar
  16. Goswami, B. B., Deshpande, M., Mukhopadhyay, P., Saha, S. K., Suryachandra, A. R., Raghu, M., et al. (2014b). Simulation of monsoon intraseasonal variability in NCEP CFSv2 and its role on systematic bias. Climate Dynamics, 43, 2725.  https://doi.org/10.1007/s00382-014-2089-5.CrossRefGoogle Scholar
  17. Griffies, S. M., Harrison, M. J., Pacanowski, P., & Rosati, A. (2004). A technical guide to MOM4, GFDL Ocean Group Tech. Rep. 5, GFDL.Google Scholar
  18. Hazra, A., Chaudhari, H. S., & Dhakate, A. (2016). Evaluation of cloud properties in the NCEP CFSv2 model and its linkage with Indian summer monsoon. Theoretical and Applied Climatology.  https://doi.org/10.1007/s00704-015-1404-3.Google Scholar
  19. Hazra, A., Chaudhari, H. S., Rao, A. S., Goswami, B. N., Dhakate, A., Pokhrel, S., et al. (2015). Impact of revised cloud microphysical scheme in CFSv2 on the simulation of Indian summer monsoon. International Journal of Climatology.  https://doi.org/10.1002/joc.4320.Google Scholar
  20. Iacono, M. J., Mlawer, E. J., Clough, S. A., & Morcrette, J.-J. (2000). Impact of an improved longwave radiation model, RRTM, on the energy budget and thermodynamic properties of the NCAR Community Climate Model, CCM3. Journal of Geophysical Research, 105, 14873–14890.CrossRefGoogle Scholar
  21. Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J. J., Fiorino, M., et al. (2002). NCEP-DEO AMIP-II Reanalysis (R-2). Bulletin of the American Meteorological Society, 83, 1631–1643.CrossRefGoogle Scholar
  22. Kang, I. S., Jin, K., Wang, B., Lau, K. M., Shukla, J., Krishnamurthy, V., et al. (2002). Intercomparision of the climatological variations of Asian Summer Monsoon precipitation simulated by 10 GCMS. Climate Dynamics, 19, 383–395.CrossRefGoogle Scholar
  23. Kang, I. S. & Shukla, J. (2005). Dynamical seasonal prediction and predictability of monsoon. The Asian Monsoon, edited by B. Wang, Praxis, Chichester, pp. 585–612.Google Scholar
  24. Malik, A., Brönnimann, S., Stickler, A., Christoph, C. R., Stefan, M., Julien, A., et al. (2017). Decadal to multi-decadal scale variability of Indian summer monsoon rainfall in the coupled ocean-atmosphere-chemistry climate model SOCOL-MPIOM. Climate Dynamics.  https://doi.org/10.1007/s00382-017-3529-9.Google Scholar
  25. Misra, V., & Li, H. (2014). The seasonal predictability of the Asian summer monsoon in a two-tiered forecast system. Climate Dynamics, 42(9/10), 2491–2507.CrossRefGoogle Scholar
  26. Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research, 102, 16663–16682.CrossRefGoogle Scholar
  27. Pan, H.-L., & Mahrt, L. (1987). Interaction between soil hydrology and boundary layer developments. Boundary-Layer Meteorology, 38, 185–202.CrossRefGoogle Scholar
  28. Pattanaik, D. R., & Kumar, A. (2010). Prediction of summer monsoon rainfall over India using the NCEP climate forecast system. Climate Dynamics.  https://doi.org/10.1007/s00382-009-0648-y.Google Scholar
  29. Pillai, P. A., & Aher, V. R. (2016). Role of monsoon intraseasonal oscillation and its interannual variability in simulation of seasonal mean in CFSv2. Theoretical and Applied Climatology.  https://doi.org/10.1007/s00704-016-2006-4.Google Scholar
  30. Pillai, P. A., Rao, S. A., George, G., Rao, D. N., Mahapatra, S., Rajeevan, M., et al. (2017). How distinct are the two flavors of El Niño in retrospective forecasts of Climate Forecast System version 2 (CFSv2)? Climate Dynamics, 48, 3829–3854.  https://doi.org/10.1007/s00382-016-3305-2.CrossRefGoogle Scholar
  31. Pokhrel, S., Dhakate, A., Chaudhari, H. S., & Saha, S. K. (2013). Status of NCEP CFS vis-a-vis IPCC AR4 models for the simulation of Indian summer monsoon. Theoretical and Applied Climatology, 111(1–2), 65–78.CrossRefGoogle Scholar
  32. Pokhrel, S., Rahaman, H., Parekh, A., Saha, S. K., Dhakate, A., Chaudhari, H. S., et al. (2012). Evaporation-precipitation variability over Indian Ocean and its assessment in NCEP Climate Forecast System (CFSv2). Climate Dynamics.  https://doi.org/10.1007/s00382-012-1542-6.Google Scholar
  33. Pokhrel, S., Saha Subodh, K., Dhakate, A., Rahman, H., Chaudhari, H. S., Salunke, K., et al. (2016). Seasonal prediction of Indian summer monsoon rainfall in NCEP CFSv2: forecast and predictability error. Climate Dynamics.  https://doi.org/10.1007/s00382-015-2703-1,2305-2326.Google Scholar
  34. Preethi, B., Kripalani, R. H., & Kumar, K. K. (2010). Indian summer monsoon rainfall variability in global coupled ocean-atmospheric models. Climate Dynamics, 35, 1521–1539.  https://doi.org/10.1007/s00382-009-0657-x.CrossRefGoogle Scholar
  35. Rajeevan, M., J. D. Kale, and J. Bhate, 2005. High-resolution gridded daily rainfall data for Indian monsoon studies, Tech. Rep. 2, Natl. Weather Serv., Pune, India.Google Scholar
  36. Rajeevan, M., Nanjundiah, R. S. (2009). Coupled model simulations of twentieth century climate of the Indian summer monsoon. In: Mukunda N (ed) Current trends in science-platinum jubilee special. Indian Academy of Science, pp. 537–567. http://www.ias.ac.in/academy/pjubilee/book.html.
  37. Rajeevan, M., Unnikrishnan, C. K., & Preethi, B. (2012). Evaluation of the ENSEMBLES multi-model seasonal forecasts of Indian summer monsoon variability. Climate Dynamics, 38, 2257–2274.  https://doi.org/10.1007/s00382-011-1061-x.CrossRefGoogle Scholar
  38. Ramu, D. A., Sabeerali, C. T., Chattopadhyay, R., Rao, D. N., George, G., Dhakate, A. R., et al. (2016). Indian summer monsoon rainfall simulation and prediction skill in the CFSv2 coupled model: Impact of atmospheric horizontal resolution. Journal of Geophysical Research, 121, 1–17.  https://doi.org/10.1002/2015jd024629.Google Scholar
  39. Ratna, S. B., Sikka, D. R., Dalvi, M., & Venkata Ratnam, J. (2011). Dynamical simulation of Indian summer monsoon circulation, rainfall and its interannual variability using a high resolution atmospheric general circulation model. International Journal of Climatology, 31(13), 1927–1942.  https://doi.org/10.1002/joc.2202.CrossRefGoogle Scholar
  40. Ratnam, J. V., Sikka, D., Kaginalkar, A., Kesarkar, A., Jyothi, N., Banerjee, S., et al. (2007). Experimental seasonal forecast of monsoon 2005 using T170L42 AGCM on PARAM Padma. Pure and Applied Geophysics, 164, 1641.  https://doi.org/10.1007/s00024-007-0242-3.CrossRefGoogle Scholar
  41. Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., & Wang, W. (2002). An improved in situ and satellite SST analysis for climate. Journal of Climate, 15, 1609–1625.CrossRefGoogle Scholar
  42. Sabeerali, C. T., Ramu, D. A., Dhakate, A., Salunke, K., Mahapatra, S., & SuryachandraRao, A. (2013). Simulation of boreal summer intraseasonal oscillations in the latest CMIP5coupled GCMs. Journal of Geophysical Research Atmosphere, 118(10), 4401–4420.  https://doi.org/10.1002/jgrd.50403.CrossRefGoogle Scholar
  43. Saha, Subodh K., Pokhrel, S., Salunke, K., Dhakate, A., Chaudhari, H. S., Rahaman, H., et al. (2016). Potential predictability of Indian summer monsoon rainfall in NCEP CFSv2. Journal of Advances in Modeling Earth Systems, 8, 1–25.  https://doi.org/10.1002/2015ms000542.CrossRefGoogle Scholar
  44. Saha, S., et al. (2010). The NCEP climate forecast system reanalysis. Bulletin of the American Meteorological Society, 91, 1015–1057.CrossRefGoogle Scholar
  45. Sahai, A. K., Chattopadhyay, R., Susmitha, J., Mandal, R., Dey, A., Abhilash, S., et al. (2015). Real-time performance of a multi-model ensemble-based extended range forecast system in predicting the 2014 monsoon season based on NCEP-CFSv2. Current Science, 109, 1802.  https://doi.org/10.18520/v109/i10/1802-1813.CrossRefGoogle Scholar
  46. Sahai, A. K., Sharmila, S., Abhilash, S., Chattopadhyay, R., Borah, N., Krishna, R. P. M., et al. (2013a). Simulation and extended range prediction of monsoon intraseasonal oscillations in NCEP CFS/GFS version 2 framework. Current Science, 104, 1394–1408.Google Scholar
  47. Sahai, A. K., et al. (2013b). Special section: Atmospheric and Oceanic sciences. Current Science, 104(10), 1394–1408.Google Scholar
  48. Seo, K.-H., Schemm, J.-K. E., Wang, W., & Kumar, A. (2007). The boreal summer intraseasonal oscillations simulated in the NCEP Climate Forecast System: The effect of Sea Surface Temperature. Monthly Weather Review, 135, 1807–1827.CrossRefGoogle Scholar
  49. Shukla, R. P., & Huang, B. (2015). Mean state and interannual variability of the Indian summer monsoon simulation by NCEP CFSv2. Climate Dynamics, 46, 3845–3864.  https://doi.org/10.1007/s00382-015-2808-6.CrossRefGoogle Scholar
  50. Sikka, D. R., & Ratna, Satyaban Bishoyi. (2011). On improving the ability of a high-resolution atmospheric general circulation model for dynamical seasonal prediction of the extreme seasons of the Indian summer monsoon. MAUSAM, 62(3), 339–360.Google Scholar
  51. Troen, L., & Mahrt, L. (1986). A simple model of the atmospheric boundary layer: Sensitivity to surface evaporation. Boundary-Layer Meteorology, 37, 129–148.  https://doi.org/10.1007/BF00122760.CrossRefGoogle Scholar
  52. Wang, B., Ding, Q., Fu, X., Kang, I. S., Jin, K., Shukla, J., et al. (2005). Fundamental challenge in simulation and prediction of summer monsoon rainfall. Geophysical Research Letters, 32, L15711.CrossRefGoogle Scholar
  53. Wang, B., Kang, I. S., & Lee, Y. J. (2004). Ensemble simulations of Asian-Australian monsoon variability during 1997/1998 El Nino by 11 AGCMs. Journal of Climate, 17, 803–818.CrossRefGoogle Scholar
  54. Winton, M. (2000). A reformulated three-layer sea ice model. Journal of Atmospheric and Oceanic Technology., 17, 525–531.CrossRefGoogle Scholar
  55. Wu, X., Simmonds, I., & Budd, W. F. (1997). Modeling of Antarctic sea ice in a general circulation model. Journal of Climate, 10, 593–609.CrossRefGoogle Scholar
  56. Yang, S., Zhang, Z., Kousky, V. E., Higgins, R. W., Yoo, S.-H., Liang, J., et al. (2008). Simulations and seasonal prediction of the Asian summer monsoon in the NCEP climate forecast system. Journal of Climate, 21, 3755–3775.  https://doi.org/10.1175/2008JCLI1961.1.CrossRefGoogle Scholar
  57. Zheng, Y., Shinoda, T., Lin, J.-L., & Kiladis, G. N. (2011). Sea surface temperature biases under the stratus cloud deck in the southeast pacific ocean in 19 IPCC AR4 coupled general circulation models. Journal of Climate, 24(15), 4139–4164.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Development of Advanced ComputingPuneIndia

Personalised recommendations