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The Role of the Basic State in Determining the Predictability of Tropical Rainfall

The Role of the Basic State in Determining the Predictability of Tropical Rainfall

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The Role of the Basic State in Determining the Predictability of Tropical Rainfall

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  1. The Role of the Basic State in Determining the Predictability of Tropical Rainfall Andrew Turner, Pete Inness and Julia Slingo. Talk Outline • Motivation. • Systematic errors in the UKMO climate model. • Flux adjustments used to correct the mean state. • Does the warmer mean state influence variability? • Future work.

  2. Motivation • South east Asian monsoon affects the lives of more than 2 billion people. • Could correcting systematic mean-state errors in coupled GCMs improve the simulated behaviour of the monsoon on all timescales? • Does this improve the prospects for seasonal and climate change prediction?

  3. UK Met Office Hadley Centre model: HadCM3 L30 • HadCM3 - atmosphere: 3.75° x 2.5° x 30 levels, - ocean: 1.25° x 1.25° x 20 levels, - integrated for 100 years. • ECMWF reanalyses ERA40 (1958-1997) • CMAP (1979-1997) rainfall data (Xie & Arkin 1997).

  4. Simulation of summer mean climate • HadCM3 simulates south-westerly monsoon flow but it is too strong, adversely affected by biases in the model. • west Pacific warm pool confined to Maritime continent and equatorial central Pacific too cool • excessive Pacific trades • Maritime Continent too warm • excessive westerly inflow to the region from the Indian Ocean via a Gill response (Gill, 1980)

  5. Flux adjustment applied over 10°N-10°S in HadCM3 • First implemented by Inness et al. (2003) to study MJO. • Annual cycle of ocean-surface heat flux adjustments is applied in the tropical Pacific and Indian oceans.

  6. Improvements to the summer (JJAS) mean state • Central equatorial Pacific warmed. • Flux adjustments reduce westward bias in the Pacific. • Excessive trades are reduced. • Much better rainfall picture over Indian Ocean, Bay of Bengal and Maritime Continent; much more like CMAP.

  7. What effect on the Variability? Regression coefficients (m/s per °C) Response of Nino4 region 10m zonal winds to Nino3 SSTs

  8. Stochastic Forcing • El Nino excited by stochastic forcing (Lengaigne et al. 2004, J. Climate, submitted). • More and stronger WWEs found in the flux adjusted model, consistent with improved MJO (Inness et al, 2003).

  9. What effect on the monsoon-ENSO teleconnection? Larger ENSO magnitude with flux adjustments, coupled with stronger trade wind response to SSTs Stronger monsoon-ENSO teleconnection. Flux adjustments also improve the timing of the Indian rainfall-ENSO teleconnection.

  10. Summary • Flux adjustments partially correct systematic biases in HadCM3, giving monsoon and Pacific systems a better mean. • Monsoon much more variable due to stronger Pacific variability and better wind response. • Monsoon-ENSO teleconnection timed better. • Greater stochastic forcing on intraseasonal timescale contributes to broader ENSO periodicity. • GCMs must have the correct basic state and the right level of stochastic forcing in the coupled system in order to accurately represent global teleconnections.

  11. Future Work • Warmer mean state has consequences for both climate and variability of monsoon systems. • Set-up and tune SPEEDY model in Reading. • Apply knowledge to results of greenhouse gas climate change integrations of SPEEDY and HadCM3.

  12. References • Codron et al. (2001) “Monsoon Dynamics: Predictability of Monsoons” Journal of Climate 14. • Gill (1980) “Some simple solutions for heat-induced tropical circulation” Q.J. Roy. Met. Soc. 106. • Inness et al. (2003) “Simulation of the Madden-Julien Oscillation in a coupled GCM part II: the Role of the Basic State” Journal of Climate 16. • Lengaigne et al. (2003) “Coupled mechanisms involved in the triggering of El Nino by a Westerly Wind Event” submitted, Journal of Climate. • Webster & Yang (1992) “Monsoon and ENSO: selectively interactive systems” Q.J. Roy. Met. Soc. 118. • Webster et al. (1998) “Monsoons: processes, predictability, and prospects for prediction” J. Geophys. Res 103.