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Simulating the extratropical response to the Madden-Julian Oscillation

Simulating the extratropical response to the Madden-Julian Oscillation. Hai Lin RPN-A, Environment Canada 46 th Congress of CMOS, Montreal May 29, 2012. Outlines. Introduction Numerical experiments: Dependence on heating location (Lin et al. 2010) Nonlinearity

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Simulating the extratropical response to the Madden-Julian Oscillation

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  1. Simulating the extratropical response to the Madden-Julian Oscillation Hai Lin RPN-A, Environment Canada 46th Congress of CMOS, Montreal May 29, 2012

  2. Outlines • Introduction • Numerical experiments: Dependence on heating location (Lin et al. 2010) Nonlinearity Dependence on initial condition • Summary

  3. Introduction • MJO • Global impact (boreal winter): NAO Canadian temperature Canadian precipitation

  4. Correlation when PC2 leads PC1 by 2 pentads: 0.66 Lin et al. (2010)

  5. Normalized Z500 regression to PC2 Lin et al. (2010)

  6. Model and experiment • Primitive equation AGCM (Hall 2000) – similar configuration of model forcing as the Marshall-Molteni model, but not Q-G. • T31, 10 levels • Time-independent forcing to maintain the winter climate • No moisture equation, no interactive convection

  7. Thermal forcing Exp1 forcing Exp2 forcing Lin et al. (2010)

  8. Z500 response Exp1 Exp2 Lin et al. (2010)

  9. Questions: • Are the responses to opposite signs of MJO forcing mirror images? (nonlinearity) • Which response is more predictable? less spread, less sensitive to initial condition and background flow? • How different are those responses to the same MJO forcing? • How does the response depend on extratropical jet initial condition?

  10. Nonlinearity • 3 sets of experiments: 1) Control 2) +MJO forcing 3) –MJO forcing • From 360 different observed initial conditions • 30-day nonlinear integrations

  11. Thermal forcing +MJO thermal forcing Exp1 forcing Exp2 forcing Lin et al. (2010)

  12. Nonlinearity Z500 response

  13. spread +MJO response has less spread, less sensitive to initial condition, thus more predictable

  14. EOF of 360 Z500 day 6-10responses to the same +MJO Downstream shift Intensify

  15. Dependence on initial condition U200 Jet intensifies Jet moves southward

  16. Summary • There is significant nonlinearity in response in mean response and spread • Response to –MJO is more sensitive to initial condition (when the heating is over central Pacific), and less predictable • Response sensitive to the strength and position of East Asian jet • Implication to subseasonal forecasting: MJO phase and jet initial condition

  17. Why the response to a dipole heating is the strongest ? • Linear integration, winter basic state • with a single center heating source • Heating at different longitudes along the equator from 60E to 150W at a 10 degree interval, 16 experiments • Z500 response at day 10

  18. 80E Day 10 Z500 linear response Similar pattern for heating 60-100E 110E 150E Similar pattern for heating 120-150W

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