1. Eastern Pacific feedbacks and the forecast of extreme El Niño events
Ciencia para protegernos
Ciencia para avanzar
Eastern Pacific feedbacks and the forecast of extreme El Niño events
Ken Takahashi
Instituto Geofísico del Perú, Lima, Peru
B. Dewitte, J. Reupo, A. Wittenberg, B. Orihuela
NOAA Climate Prediction Center, College Park, MD
November 12, 2015

2. Equatorial sea surface temperature (SST)
Commonly used ENSO indices
Equatorial sea surface temperature (SST)
West East
Sea level pressure

3. EOF-based SST anomaly patterns/indices
Takahashi et al., 2011
E pattern
C pattern
E index
C index

4. SST indices for Dec-Feb
C index
E index
Niño 3.4
Niño 1+2
The eastern Pacific warming was proportionally much more pronounced during the extreme El Niño

5. Annual rainfall in the northern coast of Peru (Piura, 5°S)
The two extreme El Niño produced as much rainfall as the next 40 rainiest years combined.

6. Sea surface temperature anomaly patterns
El Niño: East Pacific only
El Niño: Central Pacific only
Sea surface temperature anomaly patterns
Takahashi et al., 2011
Teleconnections
Local effect
Correlation with annual rainfall
Enhanced coastal rainfall
Reduced rainfall in the Andes and Amazon
Lavado y Espinoza, 2014
Atlántico
Every El Niño has a different combination of these two patterns
Pacífico

7. GFDL CM2.1 reproduces the observed E-C relationship
PC2 E E
PC1
PC1
Takahashi et al., 2011

8. Bimodal probability distribution functions for El Niño peaks*
Strong and moderate El Niño regimes in the GFDL CM2.1 climate model and observations
Bimodal probability distribution functions for El Niño peaks*
Model data for 1300 years
(250 EN peaks)
K-mean clusters in colors
Triangles = cluster centers C E
Takahashi and Dewitte, 2014
E ≈ 1.8
* EN peaks = maxima in PC1 of near-equatorial SST

9. Ocean heat budget for El Niño growth according to E
Observational
(DRAKKAR)
Model
(CM2.1)
Ocean heat budget for El Niño growth according to E
Strong EN
Strong EN
Moderate EN
Moderate EN
Phase 1:
Jan (0)-Jul (0)
Nonlinear advection contributes with 11% (obs) and 13% (CM2.1) to advective growth of strong El Niño
Phase 2:
Jul (0)-Jan (1)
Linear vertical advection
“Nonlinear dynamical heating”
(= nonlinear advection)
Linear horizontal advection
Takahashi and Dewitte, 2015

10. Observed* nonlinear Bjerknes feedback (SST/rain/wind)
Linear regression of SST (colors), OLR (contours) and wind stress on the E index in observations
Takahashi and Dewitte, 2015
* Similar in CM2.1 but shifted westwards E
All monthly data
Percentiles (10,25,50,75,90%) binned by SST indices.
Piecewise linear fit: Multivariate adaptive regression splines
East Pac OLR anom (Wm-2)
EP zonal stress anom (Nm-2)
The response in convection and wind stress to SST is more than 3 times for E > 1.5, i.e. strong eastern Pacific warming -> stronger Bjerknes feedback)

11. Strong El Niño in observations and the GFDL CM2.1 climate model
Heat content
Thermocline tilt
CP zonal stress
EP zonal stress
Obs:
CM2.1
10, 25, 50, 75, 90% percentiles
(PI control: 500 years)
Strong EN in CM2.1 very consistent with obs
Takahashi and Dewitte, 2015

12. A precursor of strong EN
Central Pac zonal wind stress anom in Aug(0)
Observations and GFDL CM2.1
Eastern Pacific warming (E) in Jan(1)
90%
10%
Takahashi and Dewitte, 2015

13. The precursor zonal wind stress is partly (but not all) given by the Bjerknes feedback
Zonal wind stress in August
Total
Coupled*
Uncoupled
*Coupled = Linear regression onto E and C
In the and events, most of the stress was “coupled”.
July Clim.
July 1982
In August 1982 there was substantial forcing from the SW Pacific (Harrison, 1984; also Hong et al., 2014)
OLR in August
Takahashi and Dewitte, 2015

14. August central Pacific zonal stress: Extreme El Niño predictor (Takahashi & Dewitte, 2015)
Approx. threshold

15. Zonal wind precursors of strong EN in CM2
Zonal wind precursors of strong EN in CM2.1 Comparing the free control and the initialized hindcasts
Obs & PI control run
Forecasts initialized in August
1982
1997
90%
10%
Takahashi and Dewitte, 2014
Dashed lines = 10% and 90% percentiles from CM2.1 PI ctl run

16. NMME* model SSTA bias-corrected forecasts (°C) of extreme El Niño (1982-83, 1997-98)
Obs
MME mean
Ensemble means of individual models
The North American Multi Model Ensemble (NMME) project hindcasts underestimated somewhat the Niño 3.4 anoms during the and El Niño, but in the Niño 1+2 the underestimation was by a factor of 3-4.
*North American Multi-Model Ensemble (Kirtman et al., 2014). Thanks to NOAA, NSF, NASA and DOE

17. GFDL CM2.1 bias-corrected forecasts* (lead 7.5)
E index
Observed
CM2.1 w/obs patterns
CM2.1 w/CM2.1 PI patterns
C index
Focusing on CM2.1 and using the E & C indices, the model did not predict E anoms during these strong EN.
Using the observed or the model’s own EOFs doesn’t make much difference.
* 10-member ensemble means

18. Relative amplitude (regression coefficient) of forecasted /observed E and C (GFDL CM2.1*, 1982-2012)
Linear regression
(relative amplitude)
Linear correlation C C
Persistence
The amplitude of the forecasted anomalies relative to the observed (based on linear regression) for different lead times show that for a lead of 3.5 months, the amplitude of E is only 20% of the observed. For C, the relative amplitude grows above unity for the first couple of months and then decreases progressively.
The forecast correlation decreases below 0.6 in one month for E and seven months for C. E E
* 10-member ensemble means

19. 2015-16 Niño 1+2 bias-corrected SST anomaly forecasts (June IC)
NASA GMAO
GFDL CM2.1 aer04 +
NOAA CFS2
Data: NMME project (Kirtman et al, 2013)

20. 2015-16 Niño 1+2 bias-corrected SST anomaly forecasts (November IC)
NASA GMAO
NASA GMAO
GFDL CM2.1 aer04
GFDL CM2.1 aer04 + +
NOAA CFS2
NOAA CFS2
Data: NMME project (Kirtman et al, 2013)

21. Summary
Extreme El Niño appears to correspond to a separate dynamical regime.
Convectively-nonlinear Bjerknes feedback in the eastern Pacific appears to be important for the growth of extreme El Niño events.
This nonlinearity, in a recharge-discharge model, can reproduce the two regimes.
Large westerly wind around August is a predictor of extreme El Niño. External wind forcing played a large role in
Ongoing research (preliminary results): Nonlinear interaction between fast model (CM2.1) drift and forecasted interannual variability shifts the threshold behaviour to the west.