1. Uncertainty of predicting El Nino-Southern Oscillation
Seon Tae Kim
Climate Prediction Department/Climate Research Team
APCC

2. What is ENSO?
The El Nino-Southern Oscillation (ENSO) describes the natural year-to-year variations in the ocean and atmosphere in the tropical Pacific.
Sea-surface temperatures in the central and eastern equatorial Pacific cycle between above- and below-average every 2 to 7 years.

3. Cont.
Peruvian fisherman observed the warmer ocean waters off of the South American coast and the impact the temperature had on their fisheries.
They named the phenomenon El Niño (the Christ child) because the effects were often seen most prominently during the Christmas season.
The Southern Oscillation part of the term ENSO refers to the atmospheric component: the shifting of atmospheric pressure between the central/eastern Pacific and the western Pacific.

4. Bjerknes Feedback
Jacob Bjerknes (1969, UCLA) suggested there exists air-ocean feedbacks which are related with the development of the tropical Pacific SST.

5. Cont.
An El Niño state occurs when the central and eastern equatorial Pacific sea-surface temperatures are substantially warmer than usual 
La Niña conditions occur when the central and eastern equatorial Pacific waters are substantially cooler than usual.

6. How Measure El Nino? Different implication for the likelihood of ENSO
NINO1+2 area: the first to warm during an El Niño
NINO3 region: experiences the most temperature variability
NINO4 regions are a strong indicator for precipitation conditions  over Indonesia
NINO3.4 index for capturing both the important SST variability and the changes of strong precipitation

7. 2015/2016 El Nino : records
1982/83
1997/98
2015/16
Nino3.4 지수로 따지면 1등 아니면 2등임.

8. ENSO Impacts
El Nino-Southern Oscillation (ENSO) events have the socio-economic impacts world wide because ENSO drives substantial change in severe weather and climate in many parts of the world.

9. ENSO Impacts (Weather)

11. The successful prediction of the ENSO at long lead times allows decision makers to consider the predicted climate variables for reducing the socioeconomic and environmental impacts.

12. Seasonal ENSO prediction

13. For predicting the El Nino occurrence a few seasons ahead, we should rely on computer models including a statistical model and dynamical climate model.

14. Current Status of ENSO prediction
Noticeable progress of models’ ability to represent ENSO (Guilyardi et al. 2009).
Improved observing and assimilation system
Improved physics (e.g., subgrid parameterization) in coupled models
Higher resolution of models
Better understanding of the tropical oceanic and atmospheric processes related with ENSO development.

15. Diverse ENSO prediction
Systematic errors in the climatological mean state in coupled models.
Errors in tropical Pacific include cold bias in the eastern Pacific, warm bias off the eastern coast , and the double ITCZ problem.
Theoretical and numerical studies showed that climatological mean state is dynamically connected with ENSO development
Their relationship is still a subject of debate.
Large Diversity of ENSO prediction in current state-of-the art coupled models, particularly the ENSO amplitude prediction.
Kim et al. (2014) showed that the upper ocean cold bias in the tropical Pacific and the associated vertical stratification is a possible reason for the diverse ENSO prediction.

16. Spring Prediction Barrier
Prediction models have a harder time making accurate ENSO forecasts.
After the spring, the ability of the models to predict increasingly better.
An average correlation coefficient form each of the 20 models between

17. Multi model system
In seasonal forecasts, the accumulated model errors become significant in comparison to the signal that is meant to be predicted. Some of those errors are shared by the different models but others are not, so combining the output from a number of models enables a more realistic representation of the uncertainties due to model error.
Studies have shown that, in most cases, such combined forecasts are more skillful than forecasts from the best of the individual models
From APEC Climate Center MME

18. Prediction error and Systematic bias

19. What controls the ENSO amplitude ?
Fedorov an Philander (2000, 2001)
Bejarano and Jin (2006)
ENSO Amplitude
Forcing & Noise
Stability
(Background State)
Nonlinearities
Jin et al. (2007)
Eisenman et al. (2005)
An (2009)
Jin et al. (2003)

20. Bjerknes coupled stability (BJ) index
(Jin et al. 2006; Kim et al. 2011a,b)
Describe atmosphere-ocean coupled dynamics in the tropical Pacific and estimate overall linear ENSO stability in a coupled system
Negative contributions by mean advection and thermal damping and positive contributions by the zonal advective, Ekman and thermocline feedbacks

21. BJ Index formulation
Partial flux form of anomalous T tendency equation
Approximated anomalous T tendency equation
Bj index formulation is started from a partial flux form of anomalous ocean temperature tendency equation.
After volume-average this equation over the eastern boxed region of the equatorial Pacific, we will get this box averaged temperature equation.
With aid of physical balance eqns based on recharge oscillaor theroy,
Based on Recharge oscillator
(Jin et al. 2006; Kim et al. 2011a)

22. BJ Index Mean Advection Damping (MA) Thermodynamic Damping (TD)
Zonal Advective
Feedback (ZA)
Ekman
Feedback (EK)
Thermocline
Feedback (TH)

23. Zonal advective feedbackτx u
(+)SSTA

24. Ekman feedback τx
(+)SSTA w

25. Thermocline feedback τx
(+)SSTA

26. Kim et al. (2015) 12 CMIP3 and 19 CMIP5 models
Historical experiments ( )
Utilized the BJ index analysis

27. Comparison of CMIP3 and CMIP5
The reduced diversity in the ENSO stability can be partly attributed to a reduced inter-model spread of the thermocline feedback and Ekman feedback terms
MA: Damping by mean advection
TD: Thermodynamic damping
ZA: Zonal advective feedback
TH: Thermocline Feedback
EK: Ekman Feedback
BJ: BJ index
ENSO Stability in CMIP5 | Seon Tae Kim

28. Cold Bias in CMIP models
Underestimated zonal advective feedback and thermocline feedback in the coupled models may be associated with too cold upper ocean temperatures
ENSO Stability in CMIP5 | Seon Tae Kim

29. Hindcast Simulation
Utilizing the hindcast simulations, we can check how the bias develops and how its development can affect the ENSO amplitude prediction.

30. APCC in-house Model NCAR CCSM3
Structure
Atmosphere
(CAM3)
Land
(CLM3)
Coupler
(CPL6)
Sea-Ice
(CSIM4)
Ocean
(POP1.4.3)
Resolution
Atmosphere
Ocean
Horizontal
T85 (1.4°x1.4°)
gx1v3(0.3°-1°)
Vertical
L26
L40

31. Initialization 3-D Ocean Nudging
In a coupled mode, simulated ocean temperatures are nudged to the 3-dimensional daily GODAS data using a restoring time scale of 5 day and simulated surface winds are nudged to observation with a restoring time scale of 1 hr.
[Rosati et al., 1987; Keenlyside et al., 2005]

32. Hindcast Simulation Ensemble members : 5 members
Ensemble generation methods: Time lagged forecasting method [Brankovic et al. 1990]
ATM/LSM/ICE ICs:
last 5 days of each month
Ocean IC: 24th of each calendar month
Each simulation performs a full 7-month integration indicating forecasts from 0-month to 6-month lead times.
Hindcast period: (33 years)

33. ENSO Amplitude and BJ index
OBS
OBS
(a) ENSO amplitude (°C) and (b) scatter plots of ENSO amplitude versus BJ index from 1-to 6-month lead times.
ENSO amplitude is defined as the standard deviation of Nino3.4 index.

34. BJ index and Feedbacks Thermocline Feedback zonal advective feedback
Ekman feedback
OBS
Mean advection damping
thermodynamic damping

35. Model drift and ENSO Amplitude
Spatial patterns of difference between climatological mean ocean temperature (colored contour lines) and its vertical gradients (shades, °C m−1)
The upper ocean cold bias and the associated vertical stratification error affect the thermocline feedback intensity and thus ENSO amplitude

36. Future ENSO?
Still no agreement on change in the amplitude of ENSO SST variability
Why ?, not clear
Collins et al. 2010

37. Models and data
Mlulti-model ensemble (MME) of selected models among 22 CMIP5 models that best simulate ENSO related air-sea feedbacks
Time-varying behavior of ENSO SST variability under historical and greenhouse warming (RCP8.5) conditions
Combined the two experiments to form a 240-year-long dataset as the RCP8.5 experiments start from 1 January 2006 of the historical runs
Analysis (e.g., climatology, linear trends, anomalies) performed over a 50-year windows moving forward starting at every year from 1861 to 2100
Anomalies are obtained by removing long-term mean seasonal cycle of each 50-year running period.
Reanalysis datasets
SODA 2.0.2, ERA40 for “Observed” BJ index
HadISST, ERSSTv2b for Observed ENSO amplitude

38. Model selection for MME analysis
Based on correlation coefficients and RMSE against observed BJ index and its contributing terms computed over the period
Nine Best Models (BEST9)

39. Projection of ENSO
The BEST9 MME shows an increasing trend of ENSO amplitude over the recent decades as the observed
r=0.90 for HadISST and r=0.87 for ERSST
The response of ENSO SST amplitude is time-varying, with increasing trend in ENSO amplitude before 2040, followed by a decreasing trend thereafter.
Inter-model standard deviation
Last year of 50-year running periods

40. Inter-model consensus, BEST9
ENSO amplitude trend (°C/65 year) of the pre- and post-2035

41. Remaining 13 less realistic models (REM13)
Persistent increase in ENSO amplitude and BJ index
r=0.73 for HadISST and r=0.67 for ERSST

42. Inter-model consensus
ENSO amplitude trend (°C/65 year) of the pre- and post-2035.
In this figure, only half of 13 less realistic models have a increasing trend both pre- and post-2035 periods.

43. Cold Bias along the equator
REM13 has more prevalent SST bias along the equator.
Observations: ERSSTv2, HadISST
SST climatology

44. Comparison with Previous El Ninos
December Mean
January Mean
EP? Or CP? Or Mixed?

45. References
Jin, F.-F., Kim, S. T., & Bejarano, L. A coupled-stability index of ENSO. Geophys. Res. Lett. 33, L23708 (2006).
Kim, S. T., & Jin, F.-F. An ENSO stability analysis. Part II: Results from 20th- and 21st-century simulations of the CMIP3 models. Clim. Dyn. 36, (2011).
Kim, S. T., Cai, W., Jin, F.-F., & Yu, J.-Y. ENSO stability analysis in coupled climate models and its association with mean state. Clim. Dyn. 42, (2014).
Kim, S. T., Cai, W., Jin, F.-F. Santoso, A., Wu, R., Guilyardi, E. & An, S.-I. Response of El Niño sea surface temperature variability to greenhouse warming, Nature Clim. Change, 4, (2014).
Kim, S. T., H.-I. Jeong, and F.-F. Jin. Mean bias in seasonal forecast model and ENSO prediction error. Scientic Reports, In print (2017).
Thank you
APEC Climate Center
Climate Research Department