Goswami Climate Change And Indian Monsoon Cse Workshop
- 1. Climate Change & Indian Monsoon : Some Key Issues B. N. Goswami (goswami@tropmet.res.in) Indian Institute of Tropical Meteorology, Pune
- 2. What is the Indian Summer Monsoon? A manifestation of seasonal northward migration of the Rain Band or Tropical Convergence Zone (TCZ )
- 4. Long term mean JJA precipitation and DJF precipitation Monsoon ? Wet- summer Dry - winter
- 5. Characteristic features of summer monsoon circulation Low level, cross-equatorial flow, south-westerlies, westerly jet in Arabian sea Upper level easterlies, Monsoon Easterly Jet Deep Baroclinic vertical structure
- 6. All India Rainfall (AIR) JJAS Mean Interannual Variability AIR Mean : 86 cm AIR S.D. : 8.6 cm No increasing trend in Monsoon rainfall past 100 yrs.
- 7. Global mean temperatures are rising faster with time Period Rate Years /decade IPCC 150 0.045 0.012 100 0.074 0.018 50 0.128 0.026 25 0.177 0.052 Warmest 12 years: 1998,2005,2003,2002,2004,2006, 2001,1997,1995,1999, 1990 ,2000
- 8. Trend in temp. is similar to global temp. trend. Much faster during past 50 years Annual mean Temp. over India 1875-2004
- 9. Indian Ocean temperature also shows warming trend
- 10. All India Rainfall (AIR) Interannual Variability No increasing trend in Monsoon rainfall past 100 yrs.
- 11. Questions : Why is the mean Indian Summer Monsoon rainfall not increasing with increasing temperature due to Climate Change? Is there some aspects of the Indian monsoon clearly affected by Climate Change?
- 12. Blue : 1950-1970 Red : 1980-2000
- 13. Time series of count over CI Low & Moderate events Heavy events (>10cm) V. Heavy events (>15cm) Goswami et al. 2006, Science, 314, 1442
- 14. Increase in intensity of extreme events Time series of av. Intensity of four largest events in a year
- 15. Why no trend in the seasonal mean ?
- 16. Global Warming makes Indian Monsoon Weather Less Predictable! Higher level of saturation of errors in the high frequency events would cascade larger errors to weather scale and could decrease the predictability of monsoon weather Neena Joseph Mani, E. Suhas and B. N. Goswami Geophys.Res. Letts (2009)
- 17. Used the daily gridded data set prepared by Rajeevan et al. 2008, Geophys. Res. Lett. Between 1901-2004 And calculated the Lyapunov Exponents over different 26 year periods
- 18. Error doubling Time for the last two quarters (1953-1978 and 1979-2004) (18N-27N, 73E -85E) Long Time series ----combined time series of all the grid points in the 3° × 4° boxes. That is, for each quarter we used a time series of length 57408 Used the daily gridded data set prepared by Rajeevan et al. 2008, Geophys. Res. Lett. Between 1901-2004 And calculated the Lyapunov Exponents over different 26 year periods
- 19. Error doubling time averaged over central India in four different quarters (solid) and number of extreme rainfall events (>15 cm/day ) Error doubling time averaged over central India in 3 different 30-year periods (solid) and number of extreme rainfall events (>15 cm/day ) Average CAPE (solid) and CINE (dashed) over Central India during the period calculated from NCEP reanalaysis
- 20. What it means A lower error doubling time means decrease in the potential predictability of monsoon weather. The monsoon weather has become almost twice as difficult to predict! Thus, effective predictions would demand a quantum leap in improvement of observations and prediction models and high computational power.
- 21. Questions : On Projections of Monsoon What will happen to the monsoon hydrological cycle 50-100 years from now under different scenarios? In particular, will the quantum of seasonal mean rainfall increase or decrease and if so by how much? What is the uncertainty in these projections? Can we quantify this uncertainty? How can we reduce this uncertainty?
- 22. Future Scenarios for Summer Monsoon Rainfall and Annual Temperature over South Asia under A2 Scenario (High Emissions) based on IPCC AR4 Simulations of AOGCMs (Anomalies relative to current period) Model spread-a rough measure of uncertainty
- 23. South Asia Kripalani et al. 2007, Theor. Appl. Clmatol.
- 24. South Asia Kripalani et al. 2007, Theor. Appl. Clmatol.
- 25. What is our confidence level that Indian monsoon will be stronger with increasing GHG and global warming? None whatsoever!! In fact, just the opposite may be possible, namely monsoon may even go to a perpetual drought state!!
- 27. Rignot et al. GRL, 2008
- 28. Climate on a threshold! Monsoon may tip to a persistent drought phase! Back Melting of Greenland Ice due to Global Warming Persistent Weak Monsoon Fresh water addition to North Atlantic Ocean Weakening of Atlantic Thermohaline Circulation Weak north-south Atmos. Temp. Gradient over Indian Longitudes
- 29. Mean=5.2 mm/day S.D. =14.7% mean LTA monsoon rainfall (JJAS) decreases by 20%!
- 30. 100 year simulation with fresh water in NA and comparison with Control. Monsoon will remain in a perpetual drought state!
- 31. Projections of monsoon under climate change scenarios by current climate models are not reliable! Because current climate models are unable to simulate the present mean monsoon climate and its variability with fidelity
- 32. Wavenumber-frequency spectra of twice daily OLR in tropics showing Kelvin, Rossby and MRG waves (Wheeler and Kiladis, JAS,1999, vol.56,374pp)
- 33. Space-time spectra of tropical waves from symmetric component of precipitation simulated by some AR4 models Observations used GPI- Global Precip Index, 2.5 x 2.5 resolution, mostly based on IR data 1DD- GPCP daily precip at 1 x 1 resolution based both IR and microwave data
- 34. Lin et al. 2006, J. Climate
- 35. NICAM
- 36. Obs. (Takayabu et al. 1999) NICAM (7-km) Matsuno (AMS, 2007)
- 37. Cloud distribution simulated by ECMWF forecast model on a given day
- 38. Tools required for reliable answer to Regional Climate Change Questions: Atmosphere GCM With Stratospheric Chemistry, Aerosol , Dynamic vegetation, resolve trop. Meososcale clouds Ocean GCM With biogeochemistry model Carbon Cycle Model
- 39. What kind of resolution may be required? Ocean GCM 1/3 x 1 O near equator and 1 O x1 O outside 10 O 50 levels in vertical Carbon Cycle Model Multicentury integration with such a model is required Computation requirements: dedicated machines with Petaflops crunching capabilities!! Atmosphere GCM 10 km global 50 levels in vertical
- 40. What are crucial requirements? Must simulate the present global climate and Indian monsoon well! The computing requirements mentioned earlier is a pre-requisite, but does not guarantee that the high resolution coupled model will simulate the present Climate and Indian monsoon well A lot of intensive research with such computing facility will be required!
- 41. In India, Where do we stand today? With the help of MoES , c omputer infrastructure is improving rapidly. However, it is still sub-critical. We are talking about 1000 times larger than the best presently available capacity! Arrival of NKN is timely and will help. However, climate modeling community in India is very small, not quite ready for modeling required for climate change! Focused Capacity Building (training) urgently required to create a pool of expert climate system modelers
- 42. Strategy at CCCR, IITM On Capacity Building Recruit new climate scientists, modelers Run a sustained in-house Training Program Get some selected young scientists trained at Centres of excellence On Estimation of Uncertainty of Projections of Monsoon First, we shall do this using multiple projections with a number of regional models with a number of global model B.Cs On reduction of uncertainty of Projections and Model Development Develop a global coupled climate model with about 50 km resolution whose monsoon climate is close to observations Make ensemble of projections with such a model
- 43. Thank You !
- 44. Unprecedented fast Variations in recent past
- 46. Basic energy spectrum (thick curve) and error energy spectrum (thin curves) Lorenz 1969 Predictability limit in any scale is the time required for the error to propagate “upscale” from very, very small initial scale to that scale and become as large as the variance in that scale.
- 47. South Asia Kripalani et al. 2007, Theor. Appl. Clmatol.
- 48. Space-time spectra of tropical waves from anti-symmetric component of precipitation simulated by some AR4 models