The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology

Weakened El Nino Predictability in the Early 21st Century Mei Zhao, Harry Hendon, Oscar Alves, Guo Liu, Guomin Wang

CAWCR/BoM Melbourne Australia

Paper in review: Nature Geosciences

Occurrences of El Nino/La Nina play primary role in Australian climate variability especially in SE during winter/spring

Capability to make extended range prediction of climate stems largely from capability to predict El Nino/La Nina

Barnston et al. 2012: reported forecast skill for range of models decreased for 2002-2011 compared to what was achieved in1980's and 90's

Why ? Chance reduction of activity or role for change in background climate?

Conundrum: Drop in skill despite forecast systems continued to improve, especially initial conditions (ARGO >2000) and improved atmospheric reanalyses

Postulate due to decrease of ENSO activity (lower signal to noise)

Results possibly skewed by inclusion of different model versions and real-time limitations of initial conditions

Revisit with a single forecast system, using research quality-controlled initial conditions

Barnston et al. 2012

9yr sliding correlation Nino3 Lt 3 (thick) and 6 (thin) mnth

9yr sliding Nino3 amplitude 1985 2005

1960-1979 1980-1999 2000-2010

Results from BoM POAMA2.4 hindcasts initialized with PEODAS using quality controlled obs data, 33 member ensemble from 1st of each month

Confirms that skill varies with amplitude. But what caused change in amplitude? Jan Jul Dec

Annual cycle Nino3 Amplitude

Recharge-discharge paradigm weakened post 1999 • Lag with Nino3 weakened and shortened (predictive capability diminished) • WWV variability reduced • Implies reduced impact of thermocline feedback for ENSO development

change in background climate contributed to drop in predictability

Basic behaviour of ENSO changed as well Lag regression Nino3 with WWV (after McPhaden 2012)

1960-1979 1980-1999 2000-2010

See also Xiang et al. 2012 Hu et al. 2013

England et al: Recent change in background climate, characterized by a swing to the cold phase of the IPO after 1999, also caused the hiatus in global surface warming

Trends 1992-2011

Trend well depicted as epochal difference (2000-2013) minus (1981-1999)

Based on PEODAS reanalysis

CMAP

NCEP reanal

Differences are small compared to interannual anomalies but significant

Design simple forecast experiment to assess impacts of background climate change on ENSO predictability and variability Circumvent possibility due to random reduction of ENSO variability Initialize earlier/stronger events with cold IPO and later/weaker events see warm IPO Use observed anomalies and mean state changes in forecast model with proven skill Let X be an observed (control) initial condition in early epoch (1985/95) and Y in later epoch (2000/10): Xc(0) = X′(0) + X�c(0)

Yc(0) = Y′(0) + Y�c(0)

Xe(0) = X′(0) + Y�c(0) = Xc(0) + ∆

Ye(0) = Y′(0) + X�c(0) = Yc(0) − ∆

∆= Y�c − X�c

Create new experiment initial conditions by making earlier epoch see mean climate of later period and vice versa

Alter initial condition for 3-d fields of T-S (ocean) and u,v,T,q (atmosphere) (as per previous slide) Rerun forecasts initialized with Xe and Ye and compare to forecasts with Xc and Yc 10 member ensemble Control and Experiment from 1st of each mnth (~120 starts, 1200 forecasts)

Change in Nino3 (Experiment minus Ctl for each epoch)

Predictability Rowell et al= (ensemble mean variance)/(ensemble mean variance + spread variance)

Observed difference (00/10 minus 85/95)

LT 1 mnth

LT 3 mnth

LT 6 mnth

Obs difference (00/10) minus (85/95)

Exp-Ctl (85/95) Exp-Ctl (00/10) Difference in standard deviation of SST

Exp-Ctl 85/95 Exp-Ctl 00/10

El Nino and La Nina equally affected In response to imposed cold IPO, El Nino and La Nina anomalies "weaken" in early period (and vice versa in later period) Regression slope is difference in growth rate (deg/mnth)

Scatter of difference in predicted N3difference (Exp-Ctl) after 1 mnth versus observed N3 at initial time

𝜕𝜕𝜕′𝜕𝜕𝜕≈ −w� 𝜕𝜕𝜕′

𝜕𝜕𝜕− u′∂T�/∂x

Δw� 1 = we 1 − wc 1 Δ𝜕𝜕T′ 1𝜕𝜕𝜕 =

𝜕𝜕Te′ 1𝜕𝜕𝜕 −

𝜕𝜕Tc′ 1𝜕𝜕𝜕

− Δw� 1𝜕𝜕Tc′ 1𝜕𝜕𝜕

+ we 1 Δ𝜕𝜕T′ 1𝜕𝜕𝜕

− uc′ 1 Δ𝜕𝜕T� 1𝜕𝜕𝜕

+ Δu′ 1𝜕𝜕T�e 1𝜕𝜕𝜕

Δ𝜕𝜕T′

𝜕𝜕𝜕 =Te′ 1 − Tobs′ 0

Δ𝜕−

Tc′ 1 − Tobs′ 0Δ𝜕

=Te′ 1 − Tc′ 1

Δ𝜕=

Linear SST growth during ENSO is primarily due to thermocline and zonal advective feedback:

Heat budget (Experiment minus Control) for first month of forecast (following Dinezio et al 2012):

Delta operator for means and perturbations defined as:

Dinezio couldn’t quantify these terms

Regress tendencies onto Nino3 at initial time to reveal contribution to initial dispersion as a result of imposed background climate change Find that rhs well approximated by (a) and (b) in main Nino region

a b c

Tendency difference (total solid; sum of three terms dotted)

Equatorial tendency differences at first month (deg/mnth) Red Exp-Ctl (2000-2010) Green Exp-Ctl (1985-1995)

Zonal advective feedback due to imposed change in mean zonal gradient

Zonal advective feedback due to change in perturbation current during forecast

Thermocline feedback due to imposed change in upwelling velocity

Difference in stand dev SST after 1 month (Exp-Ctl)

Exp-Ctl (85-95) Exp-Ctl (00-10)

Regress surface zonal wind onto Nino3

Results from a combination of colder (warmer) local SST and strengthened (weakened) Walker circulation Cold SST and sinking motion weaken coupling Warm SST and upward motion strengthen coupling

Cold "IPO" acts to weaken wind (and current) response to SST' Warm "IPO" acts to strengthen

Why did 2014 El Nino fizzle? Operational Forecasts for El Nino 2014

165 Ensemble (33 from 16, 20, 23,27,30 March)

What would have happened If 2014 saw warm IPO as in 85-95? Event would have been much stronger and more "predictable" Cold IPO resulted in El Nino 2014 that was more susceptible to noise and less predictable

Ensemble-mean warmer than verification, (especially for earlier forecasts) but weak event captured by a few members

Conclusions/Discussion • Swing to Cold "IPO" after 1999 acted to weaken ENSO predictability

First demonstration that observed mean state changes have affected predictability

Resulted from weakened coupling at heart of ENSO in east Pac

Offered as explanation for fickle behavior of 2014 El Nino

• What caused swing in "IPO" (opposite to expected change due to anthropogenic forcing, but can't rule out ocean thermostat) and there are two-way feedbacks ?

Need to think about how to address possible feedback

• Predictability of decadal variations of ENSO implied (due to connection with mean state change) no indication yet of any predictability of decadal variations of ENSO (Wittenberg et al. 2014) : but studies done with flawed models

• Will high ENSO skill scores achieved during the 1980-90s ever be achieved again?

ENSO variability was historically high in 1980's-90's

no consensus on how ENSO variability might change in future

extras

11 yr running mean: IPO index , central Pac wind stress, Nino3 amplitude in 11 yr window

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology