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

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

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

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

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. 1982-83 1997-98 1972-73

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

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

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

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

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)

Strong El Niño in observations and the GFDL CM2.1 climate model Heat content Thermocline tilt CP zonal stress EP zonal stress Obs: 1982-83 1997-98 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

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

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 1972-73 and 1997-98 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

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

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

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 1982-83 and 1997-98 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

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

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

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)

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)

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 1982-83. Ongoing research (preliminary results): Nonlinear interaction between fast model (CM2.1) drift and forecasted interannual variability shifts the threshold behaviour to the west.