Eastern Pacific feedbacks and the forecast of extreme El Niño events

Ken TakahashiInstituto Geofísico del Perú, Lima, Peru

B. Dewitte, J. Reupo, A. Wittenberg, B. Orihuela

Ciencia para protegernos Ciencia para avanzar

NOAA Climate Prediction Center, College Park, MDNovember 12, 2015

Equ

ator

ial s

ea s

urfa

ce t

empe

ratu

re (

SS

T)

Wes

tE

ast

Sea

leve

l pr

essu

re

Commonly used ENSO indices

EOF-based SST anomaly patterns/indices

Takahashi et al., 2011

E pattern C pattern

E index

C index

C in

dex

E index

SST indices for Dec-Feb

Niñ

o 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

Lavado y Espinoza, 2014

Enhanced coastal rainfall

Reduced rainfall in the

Andes and Amazon

Takahashi et al., 2011

Correlation with annual rainfall

Local effect Teleconnections

AtlánticoPacífico

El Niño: Central Pacific only

Every El Niño has a different combination of these two patterns

PC1 PC1

PC2

E

C

E

C

Takahashi et al., 2011

GFDL CM2.1 reproduces the observed E-C relationship

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

ETakahashi and Dewitte, 2014

Strong and moderate El Niño regimes in the GFDL CM2.1 climate model and observations

E ≈ 1.8

* EN peaks = maxima in PC1 of near-equatorial SST

Ocean heat budget for El Niño growth according to E

Strong EN

Moderate EN

Strong EN Moderate

EN

Phase 1:Jan (0)-Jul (0)

Phase 2:Jul (0)-Jan (1)

“Nonlinear dynamical heating” (= nonlinear advection)

Linear vertical advection

Linear horizontal advection

Nonlinear advection contributes with 11% (obs) and 13% (CM2.1) to advective growth of strong El Niño

Observational(DRAKKAR)

Model(CM2.1)

Takahashi and Dewitte, 2015

Observed* nonlinear Bjerknes feedback (SST/rain/wind)

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)

All monthly data

Percentiles (10,25,50,75,90%) binned by SST indices.

Piecewise linear fit: Multivariate adaptive regression splines

Takahashi and Dewitte, 2015

EE

Eas

t P

ac O

LR a

nom

(W

m-2)

EP

zon

al s

tres

s an

om (

Nm

-2)

Linear regression of SST (colors), OLR (contours) and wind stress on the E index in observations

* Similar in CM2.1 but shifted westwards

Takahashi and Dewitte, 2015

Strong El Niño in observations and the GFDL CM2.1 climate model

C E

Heat content

Thermocline tilt

CP zonal

stress

EP zonal stress

Obs:1982-831997-98

CM2.110, 25, 50, 75, 90% percentiles(PI control: 500 years)

Strong EN in CM2.1 very consistent with obs

A precursor of strong EN

Takahashi and Dewitte, 2015

12

Central Pac zonal wind stress anom in Aug(0)

Observations and GFDL CM2.1

Eastern Pacific warming (E) in

Jan(1)

10%

90%

The precursor zonal wind stress is partly (but not all) given by the Bjerknes feedback

Takahashi and Dewitte, 2015

In the 1972-73 and 1997-98 events, most of the stress was “coupled”.

Zonal wind stress in August

OLR in August

Total

Coupled*

Uncoupled *Coupled = Linear

regression onto E and

C

July Clim.

July 1982

In August 1982 there was substantial forcing from the SW Pacific (Harrison, 1984; also Hong et al., 2014)

August central Pacific zonal stress: Extreme El Niño predictor

(Takahashi & Dewitte, 2015)

Approx. threshold

Zonal wind precursors of strong EN in CM2.1Comparing the free control and the initialized hindcasts

Takahashi and Dewitte, 2014

Obs & PI control run Forecasts initialized in August

1982

1997

10%

90%

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)

ObsMME mean

Ensemble means of individual models

*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)

CM2.1 w/CM2.1 PI patterns

CM2.1 w/obs patternsObserved

E index

C index

* 10-member ensemble means

Relative amplitude (regression coefficient) of forecasted /observed E and C (GFDL CM2.1*, 1982-2012)

E

C

E

C

Linear regression (relative amplitude) Linear correlation

Persistence

* 10-member ensemble means

2015-16 Niño 1+2 bias-corrected SST anomaly forecasts (June IC)

GFDL CM2.1 aer04

NOAA CFS2

+

NASA GMAO

Data: NMME project (Kirtman et al, 2013)

GFDL CM2.1 aer04

NOAA CFS2

+

NASA GMAO

Data: NMME project (Kirtman et al, 2013)

2015-16 Niño 1+2 bias-corrected SST anomaly forecasts (November IC)

GFDL CM2.1 aer04

NOAA CFS2

+

NASA GMAO

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.