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Modes of variability and teleconnections: Part II

Modes of variability and teleconnections: Part II. Hai Lin Meteorological Research Division, Environment Canada Advanced School and Workshop on S2S ICTP, 23 Nov- 4 Dec 2015. Outlines. What are modes of variability? Why are they important to S2S predictions?

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Modes of variability and teleconnections: Part II

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  1. Modes of variability and teleconnections: Part II Hai Lin Meteorological Research Division, Environment Canada Advanced School and Workshop on S2S ICTP, 23 Nov- 4 Dec 2015

  2. Outlines • What are modes of variability? • Why are they important to S2S predictions? • Methods for identifying modes of variability e.g., Pacific North American (PNA) pattern, North Atlantic Oscillation (NAO) • Tropical modes of variability: ENSO, IOD, MJO, QBO, etc • Extratropical response to tropical heating • MJO-NAO interactions How do teleconnections provide sources of predictability?

  3. Atmospheric response to tropical heating • Tropics: The Gill model (Gill 1980) has proven quite successful at capturing the essential features of the tropical atmospheric response to diabaticheating • Extratropics: 1) Horizontal energy propagation of planetary waves 2) Feedback from transient eddies 3) Kinetic energy transfer from the climatological mean state

  4. Middle latitude planetary Rossby waves , , , ,

  5. Middle latitude planetary Rossby waves

  6. 500 hPa height anomaly response to an equatorial diabatic heating at dateline in a linear model with observed DJF basic flow

  7. Feedback from transient eddies

  8. Feedback from transient eddies

  9. +PNA Low frequency anomaly, e.g., PNA: Shifts jet stream and storm tracks or transient eddy activity Transient eddies feedback to PNA and reinforce the PNA Positive feedback - - + + - - −PNA + + - - + + Sheng et al. Jclim, 1998

  10. Interaction with mean flow ___

  11. 500mb geopotential height JJA DJF Central North Pacific and central North Atlantic ∂U/ ∂x < 0

  12. Atmospheric response to tropical heating In order for a climate model to have the right response to tropical heating (teleconnection) 1) a realistic structure of the diabatic heating 2) a right mean flow (small model bias) – for Rossby wave propagation and wave-mean flow interaction 3) a realistic simulation of transient eddies

  13. Connection between the MJO and NAO

  14. Data NAO index: pentad average MJO RMMs: pentad average Period: 1979-2003 Extended winter, November to April (36 pentads each winter)

  15. Composites of tropical Precipitation rate for 8 MJO phases, according to Wheeler and Hendon index. Xie and Arkin pentad data, 1979-2003

  16. Lagged probability of the NAO indexPositive: upper tercile; Negative: low tercile (Lin et al. JCLIM, 2009)

  17. Tropical influence (Lin et al. JCLIM, 2009)

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

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

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

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

  22. 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

  23. 80E Day 10 Z500 linear response Similar pattern for heating 60-100E 110E 150E Similar pattern for heating 120-150W Lin et al. MWR, 2010

  24. Impact on Canadian surface air temperature Lagged winter SAT anomaly in Canada (Lin et al. MWR, 2009)

  25. Impact on North American surface air temperature Lagged regression of SAT with −RMM2

  26. T2m anomaly composite After MJO phase 3

  27. It is possible to predict North American temperature using the MJO information • With a statistical model • For strong-MJO initial condition. Window of opportunity • Ridney et al. MWR (2013) • T(t) = a1(t)RMM1(0) + a2(t)RMM1(-1) • +b1(t)RMM2(0)+b2(t)RMM2(-1)+c(t)T(0)

  28. Fraction of correct temperature forecasts based on categories of above-, near-, and below-normal temperatures for MJO events with an amplitude > 2 in phases 3, 4, 7, and 8 with lead times of (a) 1, (b) 2, (c) 3, and (4) pentads. Rodney et al. MWR, 2013

  29. Wave activity flux and 200mb streamfunction anomaly (Lin et al. JCLIM, 2009)

  30. Two-way MJO – NAO interaction The NAO The MJO

  31. hindscast with GEM • GEM clim of Canadian Meteorological Centre (CMC)-- GEMCLIM 3.2.2, 50 vertical levels and 2o of horizontal resolution • 1985-2008 • 3 times a month (1st, 11th and 21st) • 10-member ensemble (balanced perturbation to NCEP reanalysis) • NCEP SST, SMIP and CMC Sea ice, Snow cover: Dewey-Heim (Steve Lambert) and CMC • 45-day integrations

  32. NAO forecast skillextended winter – Nov – Marchtropical influence A simple measure of skill: temporal correlation btw forecast and observations

  33. (Lin et al. GRL, 2010a)

  34. (Lin et al. GRL, 2010a)

  35. Correlation skill: averaged for pentads 3 and 4

  36. Correlation skill: averaged for pentads 3 and 4

  37. MJO forecast skill--- impact of the NAO

  38. (Lin et al. GRL, 2010b)

  39. Skill averaged for days 15-25

  40. (Lin et al. GRL, 2010b)

  41. Summary • Two-way interactions between the MJO and NAO • Lagged association of North American SAT with MJO • NAO intraseasonal forecast skill influenced by the MJO • MJO forecast skill influenced by the NAO

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