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Current Practices of Dynamical Monsoon Rainfall Prediction

Dynamical Prediction of Indian Monsoon Rainfall and the Role of Indian Ocean K. Krishna Kumar CIRES Visiting Fellow, University of Colorado, Boulder kkrishna@colorado.edu Martin P. Hoerling Climate Diagnostics Center, Boulder and Balaji Rajagopalan University of Colorado, Boulder.

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Current Practices of Dynamical Monsoon Rainfall Prediction

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  1. Dynamical Prediction of Indian Monsoon Rainfall and the Role ofIndian OceanK. Krishna KumarCIRES Visiting Fellow, University of Colorado, Boulderkkrishna@colorado.edu Martin P. HoerlingClimate Diagnostics Center, BoulderandBalaji RajagopalanUniversity of Colorado, Boulder IRI, 5 May, 2004

  2. Current Practices of Dynamical Monsoon Rainfall Prediction • 2-tiered approach wherein SSTs are predicted first using a coupled model and then the AGCMs are forced using these SST fields • Use persistent SSTs to run AGCMs • Dynamical Downscaling using Regional Climate Models taking lateral boundary values from AGCM Simulations IRI, 5 May, 2004

  3. Skills of the Present Generation of AGCMs(Reproduced from the IRI Website) IRI, 5 May, 2004

  4. We set out to examine the skills of monsoon rainfall in detail by involving long simulations made using observed SSTs with a suite of multi-model, multi-member ensemble runs. IRI, 5 May, 2004

  5. Research Questions..? • How skillful are the AGCMs in simulating Monsoon Rainfall over the Indian region? • Is specifying SSTs a constraint on realistic monsoon simulations? • How sensitive are monsoon simulations to initial conditions? • What is the impact of coupling on Monsoon-ENSO relationships? • Are the ENSO related western Indian Ocean SSTs acting as negative feed-back on Monsoon-ENSO relations? IRI, 5 May, 2004

  6. Details of AGCMs Used IRI, 5 May, 2004

  7. Simulation of Tropical Rain bands during DJF in AGCMs IRI, 5 May, 2004

  8. Simulation of Tropical Rain bands during JJA in AGCMs IRI, 5 May, 2004

  9. Climatology of Monsoon Rainfall IRI, 5 May, 2004

  10. IRI, 5 May, 2004

  11. IRI, 5 May, 2004

  12. Monsoon-ENSO Relation in AGCM Simulations IRI, 5 May, 2004

  13. IRI, 5 May, 2004

  14. Impact of Initial Conditions on Monsoon Simulations IRI, 5 May, 2004

  15. ENSO Warm-Cold Composites of Precipitation and Temperaturein CAM2 (uncoupled) and Observations IRI, 5 May, 2004

  16. IRI, 5 May, 2004

  17. IRI, 5 May, 2004

  18. Monsoon-ENSO Teleconnections: Coupled vs. Uncoupled Models IRI, 5 May, 2004

  19. GOGA: Obs SSTs globallyDTEPOGA: Obs SSTs in Deep Tropical East Pacific and Climatological SSTs elsewhereDTEPOGA_MLM: Same as DTEPOGA but a Mixed Layer Model used in the Indian Ocean IRI, 5 May, 2004

  20. Progressive Improvement in Monsoon Rainfall Simulation Skills:1. Un-coupled AMIP 2.Un-coupled AMIP only in eastern tropical Pacific and Climatological SSTs elsewhere 3.AMIP in the Pacific and Mixed Layer Model in the Indian Ocean IRI, 5 May, 2004

  21. IRI, 5 May, 2004

  22. Summary • The skills of current generation AGCMs in simulating monsoon rainfall in India even when forced with observed SSTs are very low. • However, there appears to be much higher predictive potential as evidenced by the large PERPROG skills. • No clear hint of higher skills either for models with better monsoon climatology or when multi-model-super ensembles are involved. • Specification of SSTs in the Indian Ocean appears to be the main reason for the low-skills. • An interactive ocean-atmosphere in the Indian Ocean (using even a simple mixed layer ocean model) produces more realistic monsoon simulations compared to specifying actual or climatological SSTs. • General belief that the ENSO related SSTs in the Indian Ocean (particularly the western Indian Ocean and the Arabian Sea) might act as a negative feedback on Monsoon-ENSO teleconnections appears to be wrong based on the above observations. • In general the monsoon-ENSO links are much stronger in fully coupled models compared to the AGCMs forced with observed/predicted SSTs. • The 2-tiered approach currently being pursued in seasonal forecasting needs immediate revision to achieve higher forecast skills for the Indian region. We also believe, this might be true for some other countries located in the warm pool region in the west Pacific and the Indian Ocean. IRI, 5 May, 2004

  23. K. Krishna Kumar K. Rupa Kumar, R.G. Ashrit, N.R. Deshpande and James Hansen (IRI, New York) Indian Institute of Tropical Meteorology, Pune, India (krishna@tropmet.res.in) The Climatic Impacts on Indian Agriculture IRI, 5 May, 2004

  24. Objectives • To generate data on all-India and state-level Agricultural Indices • To Identify Crops and Regions in India having strong Climatic Signal which can be used for Developing various Climate Applications initiatives/programs involving National and Multi-national Institutions and Individual Scientists • Establishing Climate Signal in various Agricultural Indices has implications for Climate Change Impact Assessment Studies as well IRI, 5 May, 2004

  25. Agriculture Facts • India lives mainly in its villages, 600,000 of them • Roughly 65% of the population is rural • India’s growth in per capita food production during 1979-92 was about 1.6% per annum – the highest in the world during this period • Agriculture provides livelihood to about 65% of the labor force • Agriculture contributes nearly 29% to the GDP • In terms of fertilizer consumption, India ranks 4th in the world • About 43% of India’s geographical area is used for agriculture IRI, 5 May, 2004

  26. IRRIGATION IRI, 5 May, 2004

  27. Production/Area/ Yield Total foodgrains Kharif/Rabi Rice Winter Wheat Groundnut Sorghum Cereals Oilseeds Pulses Sugarcane Source Agricultural Situation in India India Data Base Organizations Center for Monitoring Indian Economy Dept. of Agriculture and Cooperation, Ministry of Agriculture, Govt. of India DATA IRI, 5 May, 2004

  28. Crop Areas IRI, 5 May, 2004

  29. Monsoon Variability Features Factors IRI, 5 May, 2004

  30. All-India Summer Monsoon Rainfall (1871-2001)(Based on IITM Homogeneous Monthly Rainfall Data Set) IRI, 5 May, 2004

  31. IRI, 5 May, 2004

  32. JJA-1 SON-1 MAM DJF-1 IRI, 5 May, 2004

  33. Regional Climate Signal in Indian Agriculture Indices IRI, 5 May, 2004

  34. Area under Major Food Crops in India and % Irrigated during 1950-1998 IRI, 5 May, 2004

  35. Total Foodgrain Production in India and its Relation to Indian Rainfall IRI, 5 May, 2004

  36. Kharif Rice Production in India and its Relation to Indian Rainfall IRI, 5 May, 2004

  37. Total Wheat Production in India and its Relation to Indian Rainfall IRI, 5 May, 2004

  38. Kharif Groundnut Production and its relation to Indian Rainfall IRI, 5 May, 2004

  39. Total Sorghum Production and its relation to Indian Rainfall IRI, 5 May, 2004

  40. Global Climate Signal in Indian Agriculture IRI, 5 May, 2004

  41. IRI, 5 May, 2004

  42. IRI, 5 May, 2004

  43. IRI, 5 May, 2004

  44. IRI, 5 May, 2004

  45. IRI, 5 May, 2004

  46. IRI, 5 May, 2004

  47. IRI, 5 May, 2004

  48. IRI, 5 May, 2004

  49. Summary • Most rainfed crops show statistically significant relation with Regional and Global Climatic Factors, the exception being Sorghum. • Wheat and Sugarcane, the two most irrigated crops, do not show any climatic signal. • Groundnut and Kharif (Summer) Rice in India show very strong regional and global climatic signals and should be targeted for climate application as well as climate change impact assessment studies. IRI, 5 May, 2004

  50. Predicted and Observed Monsoon Rainfall 2002 IRI, 5 May, 2004

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