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

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.

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.

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

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.

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

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

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.

ENSO Impacts (Weather)

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.

Seasonal ENSO prediction

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

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.

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.

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 2002-2011

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 www.apcc21.org

Prediction error and Systematic bias

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)

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

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)

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

Zonal advective feedback τx u (+)SSTA

Ekman feedback τx (+)SSTA w

Thermocline feedback τx (+)SSTA

Kim et al. (2015) 12 CMIP3 and 19 CMIP5 models Historical experiments (1950-1999) Utilized the BJ index analysis

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

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

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

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

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]

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: 1982-2014 (33 years)

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.

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

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

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

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

Model selection for MME analysis Based on correlation coefficients and RMSE against observed BJ index and its contributing terms computed over the period 1950-1999. Nine Best Models (BEST9)

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

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

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

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.

Cold Bias along the equator REM13 has more prevalent SST bias along the equator. Observations: ERSSTv2, HadISST 1950-1999 SST climatology

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

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, 1593-1607 (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, 3313-3321 (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, 786-790 (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 Email: seontae.kim@apcc21.org