1. CLIVAR ENSO workshop, Paris, Nov. 2010 ENSO in GCMs: overview, progress and challenges Eric Guilyardi IPSL/LOCEAN, Paris, France & NCAS-Climate, Univ. Reading, UK 1. ENSO in coupled GCMs 2. Atmosphere feedbacks during ENSO Dynamical (Bjerknes) feedback Heat flux feedback 3. Strategies to improve ENSO in models

2. CLIVAR ENSO workshop, Paris, Nov. 2010 2 El Niño in coupled GCMs – mean state Trade winds too strong Both mean and annual cycle Guilyardi et al. (BAMS 2009) Taux Niño 4 (Pa) IPCC-class models OBS. Zonal wind stress in central Pacific (mean and annual cycle)

3. CLIVAR ENSO workshop, Paris, Nov. 2010 3 El Niño in coupled GCMs - amplitude ENSO amplitude in IPCC AR4 : much too large diversity ! Standard deviation SSTA (C) ENSO Amplitude picntrl 2xco2

4. CLIVAR ENSO workshop, Paris, Nov. 2010 4 El Niño in coupled GCMs - frequency Maximum power of Niño3 SSTA spectra AchutaRao & Sperber (2006) IPCC TAR: to high frequency CMIP3: improved towards low frequency but still large diversity

5. CLIVAR ENSO workshop, Paris, Nov. 2010 5 Westward zonal extension Too small meridional extension El Niño in coupled GCMs - structure and timing With impacts on periodicity (Capotondi et al. 2007) SST standard deviation e.g.: Model events terminate in West rather than in East Pacific Leloup et al. (2008) Time sequence of El Niño/La Niña also has errors El Niño termination spatial characteristics

6. CLIVAR ENSO workshop, Paris, Nov. 2010 6 El Niño in coupled GCMs - summary Clear improvement since ~15 years some models get Mean and Annual cycle and ENSO right ! but: Amplitude: models diversity much larger than (recent) observed diversity Frequency: progress towards low frequency/wider spectra but still errors Seasonal phase lock: very few models have the spring relaxation and the winter variability maximum Structure and timing: westward extension and narrowing around equator, issues with time sequence (onset, termination) Modes: very few model exhibits the diversity of observed ENSO modes; most are locked into a S-mode (coherent with too strong trade winds) Teleconnections: ENSO influence over-dominant Guilyardi et al. (BAMS 2009)

7. CLIVAR ENSO workshop, Paris, Nov. 2010 7 Ocean response to  and HF anomalies Upwelling, mixing, ("thermocline feedback", "cold tongue dynamics") (Meehl al. 2001, Burgers & van Oldenborgh 2003) Zonal advection (Picaut al. 1997) Wave dynamics Energy Dissipation (Fedorov 2006) van Oldenborgh al. 2005 Atmosphere response to SSTA Bjerknes wind stress feedback (van Oldenborgh al. 2005, Guilyardi 2006) Meridional response of wind stress (An & Wang 2000, Capotondi al. 2006, Merryfield 2006) Radiative and cloud feedbacks (Sun al. 2006, Bony al. 2006, Sun et al. 2009) Other processes: NL dynamical heating ( ∇ x T + U in phase, An & Jin 2004 ) "Multiplicative noise" - MJO (Lengaigne et al. 2004, Perez et al. 2005, Philip & van Oldenborgh 2007) Attributing ENSO errors: physical mechanisms

8. CLIVAR ENSO workshop, Paris, Nov. 2010 8 Other studies confirm this result (e.g. Schneider 2002, Kim et al. 2008, Neale et al. 2008, Sun et al. 2009, Shin et al. 2010,...) Atmosphere GCM has a dominant role Guilyardi et al. (2004) Q1: Why this dominant role ? (ENSO theories focus on ocean role) Q2: How to attribute ENSO errors to model systematic errors ? Attributing ENSO errors: the role of the atmosphere

9. CLIVAR ENSO workshop, Paris, Nov. 2010 9 The BJ coupled-stability index for ENSO I BJ α  : atmosphere heat flux feedback μ a : Bjerknes feedback or “coupling strength” Mean advection and upwelling (damping) Zonal advection feedback Ekman pumping feedback Thermocline feedback α  is a negative feedback (damping) μ is a positive feedback (amplification) Jin et al. (2006), Kim et al. (2010a,b)

10. CLIVAR ENSO workshop, Paris, Nov. 2010 10 Atmosphere feedbacks during ENSO Dynamical: Bjerknes feedback East-west SST gradient Trade winds Equatorial upwelling in the east Heat flux feedback SST increase in the east Modified heat fluxes (SHF, LHF)

11. CLIVAR ENSO workshop, Paris, Nov. 2010 11 Evaluating the Bjerknes feedback m Monthly variability = measure of seasonal phase lock Bjerknes amplification stronger in July-December Obs. values of m vary from 10 to 15 (10 -3 N.m -2 /C) Niño 3 SST anomaly Niño 4 TauX anomaly Seasonal evolution of m

12. CLIVAR ENSO workshop, Paris, Nov. 2010 12 Evaluating the heat flux feedback α Defined as slope of heat flux QA = F(SSTA)  varies from -10 W.m -2 /C to -30 W.m -2 /C Damping stronger in January-May Niño 3 SST anomaly Niño 3 Heat Flux anomaly

13. CLIVAR ENSO workshop, Paris, Nov. 2010 13 IPSL/Tiedke (TI) (0.3 C) – old scheme IPSL (KE) Kerry Emanuel (1.0 C) - in IPCC Impact of atmosphere convection scheme on ENSO Observations (0.9 C) - HadiSST1.1 ENSO has disappeared ! What role for α and  ? Hourdin et al. (2006) Braconnot et al. (2007) Guilyardi et al. J.Clim (2009b)

14. CLIVAR ENSO workshop, Paris, Nov. 2010 14 BJ index for KE and TI Linear theory: α dominant factor in TI/KE difference Guilyardi et al. J.Clim (2009b)

15. CLIVAR ENSO workshop, Paris, Nov. 2010 15 ~10-180.9 4-51.0 4-200.3 ma El Niño Amplitude KE TI Obs 10 -3 N.m -2 /C W.m -2 /C oCoC Compute m  and a in the KE and TI simulations Error compensation ! Can we relate this change to physical processes ? Getting the right ENSO amplitude for the wrong reasons !

16. CLIVAR ENSO workshop, Paris, Nov. 2010 16 Seasonal evolution of feedbacks Shortwave HF feedback a SW in second half of year explains most of the difference -25 W.m -2 /C 1 o C/month in SST cooling !!! (MXL 50 m, SSTA of 2 o C)

17. CLIVAR ENSO workshop, Paris, Nov. 2010 17 a SW feedback distribution Point-wise regression of SHF anomaly vs. SSTA (correl. less than 0.2 blanked out) Negative feedback (blue) = convective/ascent regime Positive feedback (red/orange) = subsidence regime ERA40 has large errors in East Pacific (Cronin et al. 2006) AMIP KE closer to ISCCP AMIP TI has too strong convection In KE, subsidence/+ve a SW invades central Pacific In TI, convection/-ve a SW invades east Pacific Coupled vs. forced (Yu & Kirtman 2007)

18. CLIVAR ENSO workshop, Paris, Nov. 2010 18 Composite analysis of large-scale regimes during El Niño Subsidence Convection in far east Pacific Positive a SW has the potential to amplify El Niño during growing phase, i.e. before convective/ascent threshold is reached AMIP KE potential much larger than that of AMIP TI KE and TI have opposite regimes during ENSO in far east Pacific TI ascent threshold 2 o C lower than KE TI physics triggers convection too easily which prevents ENSO from developping

19. CLIVAR ENSO workshop, Paris, Nov. 2010 19 Can we test this, i.e. suppress ENSO in KE ? Perform KE run with increased a SW Interannual Flux Correction: SHF O = SHF SC KE + a SW mod (SST O -SST SC KE ) a SW mod = -15 W.m -2 Mean state (SC) unchanged ma El Niño Amplitude KE TI Obs 10 -3 N/m 2 /CW/m 2 /C oCoC KE mod TI ENSO gone as well ! KE TI KE mod TI ? ~10-180.9 4-51.0 4-200.3 5-210.4

20. CLIVAR ENSO workshop, Paris, Nov. 2010 20 Summary El Niño in IPCC-class GCMs: progress but still major errors (too much diversity) and atmosphere GCM is a dominant contributor Dynamical positive ( m ) and heat flux negative (  ) feedbacks both likely to control El Niño properties in CGCMs Both feedbacks are usually too weak in models (poster by James Lloyd) In the IPSL-CM4 KE model, weak m and  compensate each other When the convection scheme is changed to Tiedke (TI),  is increased and ENSO is damped (convective regime stronger)

21. CLIVAR ENSO workshop, Paris, Nov. 2010 21 Summary cont’d Errors in these feedbacks: can be indentified in AMIP mode (good for model development) have an impact on the mean as well (improve the mean and you’ll improve ENSO) Improving ENSO in coupled GCMs requires: Getting the right amplitude for the right reasons ! Increased atmosphere GCM horizontal resolution to improve bjerknes feedback m Improved atmosphere convective physics (value of  SHF- ) Improved low clouds feedbacks (value of  SHF+ ) Reduced cold tongue bias (spatial extent of  SHF+ ) Validate ENSO feedbacks during model development phases Analysis method clearly attributes model systematic biases to errors in ENSO atmosphere feedbacks

22. CLIVAR ENSO workshop, Paris, Nov. 2010 22 How can we improve ENSO in models ? Improve the quality and utility of historical records Maintain present ENSO observing system into the future Continue promoting intercomparison studies (ENSO metrics) Isolate the main sources of model error, guided by theory (BJ), observations, and rigourous evaluation of the models, including tests in seasonal forecast mode (talk by Benoît Vannière) A few challenges lying ahead:

23. CLIVAR ENSO workshop, Paris, Nov. 2010 23 Amplitude error in Niño 3 and Niño 4 ENSO Frequency RMS Zonal wind RMS error in Equatorial PacificSST annual cycle amplitude error in Niño 3 ENSO performance metrics Devised by CLIVAR Pacific Panel WG for CMIP5 a few examples...

24. CLIVAR ENSO workshop, Paris, Nov. 2010 24 How can we improve ENSO in models ? Improve the quality and utility of historical records Maintain present ENSO observing system into the future Continue promoting intercomparison studies (ENSO metrics) Isolate the main sources of model error, guided by theory (BJ), observations, and rigourous evaluation of the models, including tests in seasonal forecast mode (talk by Benoît Vannière) A few challenges lying ahead: Should we revisit the role of atmosphere processes in ENSO theory (non-linear feedbacks, WWB,...) ? Are observational records long/precise enough ? Thank you ! Other challenges:

25. CLIVAR ENSO workshop, Paris, Nov. 2010 25

26. CLIVAR ENSO workshop, Paris, Nov. 2010 26 ENSO atmosphere feedbacks: what about m ? 12-180.9 3.9-4.90.8 4.4-5.10.95 6.2-5.71.15 7.5-4.31.13 7.8-51.18 ma El Niño Amplitude Varying the horizontal atmosphere resolution in IPSL-CM4 Obs 10 -3 N/m 2 /CW/m 2 /C oCoC R97E (3.75 x 2.5) R99A (3.75 x 1.8) R149A (2.5 x 1.8) R1414A (2.5 x 1.2) R1914E (1.8 x 1.2) AGCM resolution affects m (but not a ): Atmosphere grid can “see” ocean equatorial wave guide Added non-linearities in AGCM: better circulation (on/off convection behavior reduced,...) (Zonal x Merid.)

27. SW (blue bars) and LH (green bars) components dominate. The SW feedback shows the greatest model diversity – linked to cloud uncertainties in East Pacific region (e.g. Sun et al. 2009). Split net feedback into four components: shortwave (SW), longwave (LW), latent heat (LH) and sensible heat (SH) flux feedbacks. Lloyd et al. (2009) Heat flux a feedback components

28. CLIVAR ENSO workshop, Paris, Nov. 2010 28 SST threshold for ascent/convection Bin vertical velocity at 500 hPa in SST bins Ascent/Convective threshold when regime switches from subsidence to convection (Bony et al. 2004) Subsidence Convection AMIP KE threshold larger by 1 o C / AMIP TI (same SST !) KE threshold unchanged TI threshold even lower: 2 o C difference with KE ! TI physics triggers convection too easily which prevents ENSO from developping

29. CLIVAR ENSO workshop, Paris, Nov. 2010 29 Bjerknes feedback  Bjerknes feedback in AMIP KE and AMIP TI similar and within re-analysis estimates Bjerknes feedback in KE = 1/3 rd of that of AMIP KE

30. CLIVAR ENSO workshop, Paris, Nov. 2010 30 Heat flux feedback α Heat flux feedback α in AMIP TI is double that of AMIP KE (and closer to re-analysis estimates) Heat flux feedback in KE = half of that of AMIP KE Value unchanged in TI

31. CLIVAR ENSO workshop, Paris, Nov. 2010 31 Evolution of El Nino composite Wind Stress Wind stress anomaly (shading) SST anomaly (solid contours) 3 year composite at equator Bjerknes feedback in AMIP KE and AMIP TI similar Bjerknes feedback in KE much weaker than in AMIP KE

32. CLIVAR ENSO workshop, Paris, Nov. 2010 32 Evolution of El Nino composite Heat Flux Heat flux anomaly (shading) SST anomaly (solid contours) 3 year composite at equator Negative Heat Flux anomaly in AMIP TI much larger than in AMIP KE Negative Heat Flux anomaly during positive SST anomaly Negative Heat Flux anomaly in KE too far west and weak SHF main contributor to differences between AMIP TI and AMIP KE (LHF also contributes)

33. CLIVAR ENSO workshop, Paris, Nov. 2010 33 Mean seasonal cycle at Eq. AMIP KE performs rather well Convection in AMIP TI too strong and triggered too soon Biases amplified in coupled mode Semi-annual cycle in TI Equinoctial Central American monsoon too strong in TI (Braconnot et al. 2007) Wind stress (shading) SST (solid contours) Precipitation (3 and 8 mm/day dashed)