Presentation on theme: "The Indian Summer Monsoon and Climate Change Andrew Turner with Pete Inness & Julia Slingo RMetS meeting, Wednesday 20 June 2007."— Presentation transcript:
The Indian Summer Monsoon and Climate Change Andrew Turner with Pete Inness & Julia Slingo RMetS meeting, Wednesday 20 June 2007
Introduction Indian summer monsoon affects the lives of more than 2 billion people across South Asia, and provides more than 75% of total annual rainfall. Agricultural and, increasingly, industrial consumers require reliable source of water, together with an appropriate forecast on seasonal and intraseasonal timescales. How monsoon characteristics may change in the future is a key goal of climate research.
Outline Introduction Model framework Climate change and the mean monsoon Interannual and intraseasonal variability How do systematic model biases affect the result? The monsoon-ENSO teleconnection
Model set-up Hadley Centre coupled model HadCM3 run at high vertical resolution (L30). This better represents intraseasonal tropical convection 1 and has an improved atmospheric response to El Niño 2. 1 P.M. Inness, J.M. Slingo, S. Woolnough, R. Neale, V. Pope (2001). Clim. Dyn. 17: H. Spencer, J.M. Slingo (2003). J. Climate 16: Control (1xCO 2 ) and future climate (2xCO 2 ) integrations used to test the impact of increased GHG forcing. Further integration of each climate scenario to test the role of systematic model biases.
2xCO 2 response of HadCM3 Summer climate of HadCM3 2xCO 2 Response to 2xCO 2
The monsoon in IPCC AR4 models Annamalai et al. (2007): Of the six AR4 models which reasonably simulate the monsoon precipitation climatology of the 20 th century, all show general increases in seasonal rainfall over India in the 1pctto2x runs (including HadCM3 L19). H. Annamalai, K. Hamilton, K. R. Sperber (2007). J. Climate 20:
Interannual variability Exceptional seasons of persistent flood or drought have devastating economic and human consequences. Interannual variability is projected to increase at 2xCO 2 (+24%), particularly through increased likelihood of very wet seasons. PDF of seasonal rainfall over India in HadCM3.
Intraseasonal variability Intraseasonal monsoon variations are arguably of most importance to local populations, active and break events bringing intense rains and short droughts to monsoon regions. The extended and intense break of July 2002 contributed to nationwide drought with 19% reduction in JJAS rainfall from climatology. Source:
Intraseasonal variability Changes to active-break cycles at 2xCO 2 : break events Break events defined where AIR daily precip falls 1σ below the mean. More intense break events over India at 2xCO 2 (and active events, not shown). Various indices tested. Break event precipitation anomalies to annual cycle: 2xCO 2 minus 1xCO 2 Caveats?
Intraseasonal variability Changes to heavy precipitation Levels of heavy precipitation increase at upper percentiles in 2xCO 2 climate. Changes are beyond those due to the change in mean precipitation. Precipitation values at upper percentiles 1xCO 2 2xCO 2
Model set-up Hadley Centre coupled model HadCM3 run at higher vertical resolution (L30 vs. L19). This better represents intraseasonal tropical convection 1 and has an improved atmospheric response to El Niño 2. 1 P.M. Inness, J.M. Slingo, S. Woolnough, R. Neale, V. Pope (2001). Clim. Dyn. 17: H. Spencer, J.M. Slingo (2003). J. Climate 16: Control (1xCO 2 ) and future climate (2xCO 2 ) integrations used to test the impact of increased GHG forcing. Further integration of each climate scenario to test the role of systematic model biases.
Systematic biases in HadCM3 Summer climate of HadCM3 1xCO 2 HadCM3 minus observations
Flux adjustments at 1xCO 2 Flux adjustments are calculated by relaxing Indo- Pacific SSTs back toward climatology in a control integration. The heat fluxes required for the relaxation are saved and meaned to form an annual cycle. Annual cycle applied to the equatorial band of a new integration*. Annual Mean Amplitude of annual cycle * After: P.M. Inness, J.M. Slingo, E. Guilyardi, J. Cole (2003). J. Climate 16:
Systematic biases in HadCM3 & their reduction in HadCM3FA Maritime Continent cooled; cold tongue warmed Coupled response: reduced trade wind errors and monsoon jet Reduced convection over Maritime Continent & other precip errors opposed HadCM3 minus observations HadCM3FA minus HadCM3 Results from A.G. Turner, P.M. Inness, J. M. Slingo (2005) QJRMS 131:
Flux adjustments at 2xCO 2 Assume systematic biases will still be present in the future climate. Assume that the adjustments necessary to correct these biases will be the same. Same annual cycle of flux adjustments used at 2xCO 2 (in common with previous studies where adjustments were necessary to combat drift).
2xCO 2 response of HadCM3 Summer climate of HadCM3 2xCO 2 Response of HadCM3 2xCO 2
2xCO 2 response of HadCM3FA Summer climate of HadCM3FA 2xCO 2 Response of HadCM3FA to 2xCO 2
Monsoon precipitation response Systematic bias seems to mask full impact of changing climate Taken from A.G. Turner, P.M. Inness, J.M. Slingo (2007). QJRMS, accepted, due out soon
Monsoon-ENSO teleconnection: lag-correlations Flux adjustments have dramatic impact on the teleconnection, particularly when measured by Indian rainfall. The impact of increased GHG forcing is less clear but the teleconnection is generally robust. DMIIndian rainfall
Summary Projections of the future climate show robust / enhanced mean monsoon consistent with other modelling studies. Intraseasonal and interannual modes of variation are more intense at 2xCO 2, potentially leading to greater impacts of the monsoon on society. Systematic model biases may be masking the true impact of increased GHG forcing. The monsoon-ENSO teleconnection, useful for seasonal prediction, remains robust. Indeed model error has more impact.
Monsoon-ENSO teleconnection: moving correlations Variations of correlation strength in models are of similar amplitude to those seen in observations despite fixed CO 2 forcing. See also AR4 models in Annamalai et al. (2007). HadISST vs. All-India gauge data rainfall DMI