1. ENSO Frequency Cascade and Implications for Predictability
Fei-Fei Jin
University of Hawaii
FOCRAII 2017

2. Basic Idea: By interacting with the annual cycle of the climate system, ENSO can generate impacts on timescales much faster than ENSO own timescale via nonlinear frequency cascade and thus lead to a deterministic transformation of ENSO signal to subs-seasonal to sub-annual predictability.

3. Example #1: ENSO-frequency Cascade and Predictability of ENSO impact on East Asia Monsoon

4. Pathways from ENSO to impact East Asia monsoon through WNP
El Niño ?
PJ or EAP (Nita 1987, Huang et al 1987)

5. Observed Evidence ENSO forcing Stuecker et al. 2013, Nature Geoscience
meridionally antisymmetric wind response,on different timescales than the forcing
[m s-1]
meridionally symmetric wind response on same timescale as the forcing
[m s-1]
Stuecker et al. 2013,
Nature Geoscience

6. updated Stuecker et al. 2013, Nature Geoscience Data: ERA interim
PC2 SIMPLE = PC1(t)* cos (wAt)
EOF/PC 2 emerges from the nonlinear interaction between
ENSO and the warm pool annual cycle!
updated Stuecker et al. 2013,
Nature Geoscience
Data: ERA interim

7. Part 1: A combination mode (C-mode) of ENSO and the annual cycle
Modeling Evidence
Wind PC2 is an atmospheric combination mode of ENSO and the annual cycle!
Perpetual Experiment (PERP):
Wind PC2 is not generated if the annual cycle is removed from the forcing!
Part 1: A combination mode (C-mode) of ENSO and the annual cycle

8. C-mode=Nion3.4(t)* cos (wAt)!
The role of air-sea thermal dynamic feedback/teleconnection
is secondary.

9. (A) ENSO SST anomaly forcing pattern for the AGCM experiments.
(A) ENSO SST anomaly forcing pattern for the AGCM experiments. The amplitude of the anomaly pattern is given by the colored shading (no units). The N3.4 (black) and NWP (cyan) regions are marked by two boxes. (B) The time evolution of the N3.4 SST anomaly index (black) and the anomalous ensemble mean circulation index NWP−AC(t)¯ (cyan) for the 2.5-y experiment.
Malte F. Stuecker et al. PNAS 2015;112:
©2015 by National Academy of Sciences

11. ENSO frequency cascade
Deterministic transfer from power on interannual timescales (ENSO) to higher frequencies through nonlinear ENSO/annual cycle interactions
realistic exp
Stuecker et al. 2015,
PNAS

12. Increased Explained Variance of Monsoon precipitation (China) due to El Nino-C and predictability in season transitions
The well-known two jumps of rain bands over China
in summer may be predictable once slow ENSO
evolution is predicted.

14. An New Hypothesis for Indian Ocean Dipole (IOD) Mode:
Example #2 : ENSO frequency cascade and Predictability of Indian Ocean Dipole
An New Hypothesis for Indian Ocean Dipole (IOD) Mode:
IOD is forced by ENSO-C mode

15. An simplest IOD model: Stuecker et al. 2017, ENSO C-mode forcing
Seasonally modulated
damping
ENSO C-mode forcing
+ stochastic forcing
The physical basis for this model is that the anomalous surface wind stress and heat fluxes induced by the atmospheric ENSO C-mode circulation in the eastern Indian Ocean are represented by the right hand side forcing term in equation.
Stuecker et al. 2017,

16. IOD Observation & Reconstruction (1982-2015)
Figure 1. Temporal and spatial variation of Indian Ocean Dipole

18. IOD Observation & Reconstruction (1982-2015)
Figure 1. Temporal and spatial variation of Indian Ocean Dipole

19. Hindcast experiments Observations OI SST v2, 1982-2015
Nino 3.4 & IOD index (DMI)
NCEP CFSv2 seasonal forecasts
24 members and ensemble mean
Climatology & Hindcast period,
Hindcast experiments
Persistence
SDM Zero Forcing (SDM-Z)
SDM Perfect Cmode Forcing (SDM-P)
SDM Forecast Cmode Forcing (SDM-F)
CFSv2
Compare the forecast correlation skill and RMSE from CFSv2 & SDM models

20. IOD prediction skill for whole months (1982-2015)
Correlation Skill
RMSE Skill
SDM model with perfect C-mode forcing (SDM-P) exhibits better skill than CFSv2 and Persistence;
SDM-F is as good as GFSv2 forecast in leading 1-3 months and SDM-F is better than GFSv2 after leading 4 months;
The differences between SDM-P and SDM-Z (zero forcing) shows the seasonal modulated ENSO driven predictability.

21. DMI monthly mean forecast
SDM-P
Statistical-Dynamical Model demonstrably better forecast skill than CFSv2: 1992, 1996, 2015.
SDM-F
CFSv2 +
Lead (months)
In 1994 event of Non-ENSO year, SDM exhibit equal forecast skill with CFSv2. (at least 3 months ahead)
Obs.
In the bottom + +
+ pIOD + + +

22. Example #3 : PNA & NAO？

23. ENSO is the main source of predictability.
Summary
ENSO is the main source of predictability.
ENSO-frequency Cascade transforms the slow ENSO signal
In anquasi-deterministc manner into near annual and
sub-annual timescales responses which may lead to certain
predictability on these timescales.

24. 2016 Summer Flood in China
Death toll
about 1000