ENSO nonlinearity in a warming climate Julien Boucharel LEGOS / Univ. Toulouse, France Dewitte B., du Penhoat Y. LEGOS / IRD, Toulouse, France Garel B. IMT / Univ. Toulouse, France Yeh S.-W. DEMS, Hanyang Univ., Ansan, South Korea Kug J.-S. KORDI, Hanyang Univ., Ansan, South Korea

ENSO Clivar Workshop, Paris, November 2010 Why studying ENSO nonlinearity ? LINEAR theories have provided an understanding of the MAIN mechanisms leading to:  the growth/decay of the initial SST perturbation  Bjerknes feedback (Bjerknes, 1966 )  the oscillatory nature of ENSO  Delayed negative feedback of oceanic dynamic adjustment (e.g. equatorial wave dynamics) (Cane and Zebiak, 1985; Schopf and Suarez, 1988; Battisti and Hirst, 1989 …)

ENSO Clivar Workshop, Paris, November 2010 LINEAR theories have provided an understanding of the MAIN mechanisms leading to:  the growth/decay of the initial SST perturbation  Bjerknes feedback (Bjerknes, 1966 )  the oscillatory nature of ENSO  Delayed negative feedback of oceanic dynamic adjustment (e.g. equatorial wave dynamics) (Cane and Zebiak, 1985; Schopf and Suarez, 1988; Battisti and Hirst, 1989 …) Regular and periodic oscillatory mode over a wide range of parameters. BUT ….. Why studying ENSO nonlinearity ?

Strongly irregular behaviour of ENSO related timeseries ENSO Clivar Workshop, Paris, November 2010 SSTA [°C] Time [Year] Niño3 SST anomalies from Kaplan reconstruction (Kaplan et al., 1998).

Slowly varying mean state Inter-decadal variability ENSO Clivar Workshop, Paris, November 2010 SSTA [°C] Time [Year] Niño3 SST anomalies from Kaplan reconstruction (Kaplan et al., 1998). 12-years running mean

Inter-decadal variability reflected by the presence of abrupt transitions (Climate shifts) Bivariate test for the detection of a systematic change in mean (shift) Maronna and Yohai (1978), Potter (1981), Boucharel et al. (2009) ENSO Clivar Workshop, Paris, November Cold shift 1976 Warm shift 1998 Cold shift SSTA [°C] Time [Year] Niño3 SST anomalies from Kaplan reconstruction (Kaplan et al., 1998).

ENSO Clivar Workshop, Paris, November 2010 Homogenous periods in terms of ENSO characteristics: - Variability - Frequency - Asymmetry- Predictability … SSTA [°C] Time [Year] Niño3 SST anomalies from Kaplan reconstruction (Kaplan et al., 1998). Positive asymmetry Skewness > 0 Pseudo symmetry Skewness ~ 0 Positive asymmetry Skewness > 0 Pseudo symmetry Skewness ~ 0

Presence of "anomalous" Extreme Events Complexity of ENSO system on a wide range of timescales ENSO Clivar Workshop, Paris, November 2010 SSTA [°C] Time [Year] Niño3 SST anomalies from Kaplan reconstruction (Kaplan et al., 1998).

ENSO Clivar Workshop, Paris, November 2010 (Inter-)decadal changes of ENSO characteristics mirror (inter-)decadal changes of nonlinearity (as measured by ENSO asymmetry). An and Wang, 2000; Wu and Hsieh, 2003; An, 2004; Ye and Hsieh, 2006…. Dynamical linkage between these changes (An, 2009) Nonlinear processes are part of the ENSO system and may be involved in ENSO low-frequency modulation Why studying ENSO nonlinearity ?

ENSO Clivar Workshop, Paris, November 2010 Outline: 1. Measuring the nonlinearity of ENSO 2. Interactive feedback between ENSO irregularity and low-frequency variability 3. ENSO statistics in a warming climate 4. Conclusions, perspectives

ENSO Clivar Workshop, Paris, November 2010 Outline: 1. Measuring the nonlinearity of ENSO 2. Interactive feedback between ENSO irregularity and low-frequency variability 3. ENSO statistics in a warming climate 4. Conclusions, perspectives

ENSO Clivar Workshop, Paris, November 2010  Statistical measure Smoothed histogram of monthly SST anomalies ( ) averaged in Niño3 [°C] Number of occurences Summary of ENSO statistical properties: Probability Density Function Gaussian curve corresponding to the best sampled PDF fit. Quantifying ENSO nonlinearity

ENSO Clivar Workshop, Paris, November 2010  Statistical measure Smoothed histogram of monthly SST anomalies ( ) averaged in Niño3 [°C] Number of occurences Summary of ENSO statistical properties: Probability Density Function Presence of Extreme Events… Quantifying ENSO nonlinearity

ENSO Clivar Workshop, Paris, November 2010  Statistical measure Smoothed histogram of monthly SST anomalies ( ) averaged in Niño3 [°C] Number of occurences Summary of ENSO statistical properties: Probability Density Function Presence of Extreme Events… and a strong positive asymmetry Quantifying ENSO nonlinearity

ENSO Clivar Workshop, Paris, November 2010  Statistical measure Smoothed histogram of monthly SST anomalies ( ) averaged in Niño3 [°C] Number of occurences Summary of ENSO statistical properties: Probability Density Function Presence of Extreme Events… and a strong positive asymmetry Contraction of the PDF near 0 Quantifying ENSO nonlinearity

But other ENSO statistical peculiarities have to be taken into account  Need to propose a quantification of the presence of EE, the leptokurtic deformation and the asymmetry of ENSO PDF  Higher order statistics Hypothesis: - The "distorsion" of the PDF of tropical Pacific climate variables is a signature of the presence of nonlinearity in the ENSO system.  Quantifying this distorsion can provide insights on an integrated level of ENSO nonlinearity ENSO Clivar Workshop, Paris, November 2010 Up to now, only ENSO asymmetry (skewness) has been considered to document the tropical pacific nonlinearity (Burgers and Stephenson, 1999; An and Jin, 2004) Quantifying ENSO nonlinearity

ENSO Clivar Workshop, Paris, November 2010  ENSO statistical specificities prompt us to consider heavy-tails laws family: Quantifying ENSO nonlinearity

ENSO Clivar Workshop, Paris, November 2010 The  -stable law, an example of the wide heavy-tails laws family: Lévy (1924); Mandelbrot (1960, 1963). Benoît Mandelbrot ( ) Quantifying ENSO nonlinearity  ENSO statistical specificities prompt us to consider heavy-tails laws family:

ENSO Clivar Workshop, Paris, November 2010 The  -stable law, an example of the wide heavy-tails laws family: Lévy (1924); Mandelbrot (1960, 1963). Benoît Mandelbrot ( ) Quantifying ENSO nonlinearity  ENSO statistical specificities prompt us to consider heavy-tails laws family: 4 parameters govern stable distributions  and  Characteristic function:

ENSO Clivar Workshop, Paris, November 2010 The  -stable law, an example of the wide heavy-tails laws family: Lévy (1924); Mandelbrot (1960, 1963). Benoît Mandelbrot ( ) Quantifying ENSO nonlinearity  ENSO statistical specificities prompt us to consider heavy-tails laws family: 4 parameters govern stable distributions     and  Characteristic function:

  -stable laws, examples ENSO Clivar Workshop, Paris, November 2010 Quantifying ENSO nonlinearity  = 0  = 0  = 1 Dependance on 

  controls the leptokurtic deformation of the PDF   associated with the kurtosis (≥ 4 th -order statistical moment)   -stable laws, examples ENSO Clivar Workshop, Paris, November 2010 Quantifying ENSO nonlinearity  = 0  = 0  = 1 Dependance on 

  -stable laws, examples ENSO Clivar Workshop, Paris, November 2010   controls the leptokurtic deformation of the PDF   associated with the kurtosis (≥ 4 th -order statistical moment)   associated with the skewness (= 3 rd -order statistical moment)  = 1.2  = 0  = 1  = 0  = 0.5  = 0.8  = 1 Quantifying ENSO nonlinearity  = 0  = 0  = 1 Dependance on  Dependance on 

  -stable laws, examples  = 2   = 0 Gaussian distribution ENSO Clivar Workshop, Paris, November 2010   controls the leptokurtic deformation of the PDF   associated with the kurtosis (≥ 4 th -order statistical moment)   associated with the skewness (= 3 rd -order statistical moment)  = 0  = 0.5  = 0.8  = 1 Quantifying ENSO nonlinearity  = 0  = 0  = 1 Dependance on  Dependance on   = 1.2  = 0  = 1

Koutrouvelis (1980): Regression method using the sample characteristic function:  Estimation of  -stable parameters Rigorous statistical framework to quantify equivalent of high order statistical moments of ENSO timeseries Metrics of nonlinearity ENSO Clivar Workshop, Paris, November 2010 Quantifying ENSO nonlinearity

 Estimation of  -stable parameters ENSO Clivar Workshop, Paris, November 2010    -stable parameters inferred from Kaplan reconstruction SST anomalies on the period. Quantifying ENSO nonlinearity

 Estimation of  -stable parameters ENSO Clivar Workshop, Paris, November 2010    During the last 130 years, most of the tropical Pacific exhibit  -stable properties. Coherent with other reconstructions (HadSST, ERSST) Gaussian features  -stable parameters inferred from Kaplan reconstruction SST anomalies on the period. Quantifying ENSO nonlinearity

 Estimation of  -stable parameters ENSO Clivar Workshop, Paris, November 2010    During the last 130 years, most of the tropical Pacific exhibit  -stable properties. Coherent with other reconstructions (HadSST, ERSST)  Particularly the Warm Pool and the Cold Tongue regions Gaussian features  -stable parameters inferred from Kaplan reconstruction SST anomalies on the period. Quantifying ENSO nonlinearity

 Estimation of  -stable parameters ENSO Clivar Workshop, Paris, November 2010    -stable parameters inferred from Kaplan reconstruction SST anomalies on the period.  During the last 130 years, most of the tropical Pacific exhibit  -stable properties. Coherent with other reconstructions (HadSST, ERSST)  Particularly the Warm Pool and the Cold Tongue regions  The asymmetry map exhibits a zonal see-saw pattern Gaussian features Quantifying ENSO nonlinearity

ENSO Clivar Workshop, Paris, November 2010 Outline: 1. Measuring the nonlinearity of ENSO 2. Interactive feedback between ENSO irregularity and low- frequency variability 3. ENSO statistics in a warming climate 4. Conclusions, perspectives

ENSO Clivar Workshop, Paris, November 2010 Inter-decadal changes of ENSO nonlinearity SSTA [°C] Time [Year] Estimation of  and  on each period

ENSO Clivar Workshop, Paris, November 2010  Estimation of  -stable law main parameters (Boucharel et al., 2009): : : : :  Distinct nonlinear behaviours according to the tropical mean state  Inter-decadal changes of ENSO nonlinearity

ENSO Clivar Workshop, Paris, November 2010  Estimation of  -stable law main parameters (Boucharel et al., 2009): : : : :  Distinct nonlinear behaviours according to the tropical mean state  Alternation of periods favouring Extreme Events triggering in the Cold Tongue with other in the Warm Pool  Inter-decadal changes of ENSO nonlinearity

ENSO Clivar Workshop, Paris, November 2010  Estimation of  -stable law main parameters (Boucharel et al., 2009): : : : :  Distinct nonlinear behaviours according to the tropical mean state  Alternation of periods favouring Extreme Events triggering in the Cold Tongue with other in the Warm Pool  Low frequency modulation of the nonlinearity imprint in the tropical Pacific  Inter-decadal changes of ENSO nonlinearity

ENSO Clivar Workshop, Paris, November 2010 ENSO - Asymmetry - Extreme Events Mean state Inter-decadal 20 – 50 years INTER-SHIFT periods Inter-decadal Modulation Irregularity Low frequency High frequency Inter-decadal changes of ENSO nonlinearity

ENSO Clivar Workshop, Paris, November 2010 ENSO - Asymmetry - Extreme Events Mean state Inter-decadal 20 – 50 years INTER-SHIFT periods Inter-decadal Modulation Irregularity 2-ways feedback ? Is the ENSO irregularity associated with Extreme Events able to act back on the tropical Pacific mean state? ? Low frequency High frequency Inter-decadal changes of ENSO nonlinearity

ENSO Clivar Workshop, Paris, November 2010 Fig. 1. Annual cycle of variance (dashed line; scales in the right y- axis) and skewness (solid line; scales in the left y-axis) of Niño-3 index obtained from ERSST data averaged over 1880 to An and Choi (2009) Decadal changes in the seasonality of the ENSO asymmetry may influence the decadal changes in the amplitude of the annual and semi-annual cycles, and therefore the tropical Pacific decadal mean state. Inter-decadal changes of ENSO nonlinearity

ENSO Clivar Workshop, Paris, November 2010 Fig. 1. Annual cycle of variance (dashed line; scales in the right y- axis) and skewness (solid line; scales in the left y-axis) of Niño-3 index obtained from ERSST data averaged over 1880 to Decadal changes in the seasonality of the ENSO asymmetry may influence the decadal changes in the amplitude of the annual and semi-annual cycles, and therefore the tropical Pacific decadal mean state. This is because the season- dependent nonlinear rectification can modify the annual and semi-annual cycles. Inter-decadal changes of ENSO nonlinearity An and Choi (2009)

ENSO Clivar Workshop, Paris, November 2010 An and Choi (2009) Dewitte et al. (2007) Timmermann et al. (2003) season-dependant Inter-decadal changes of ENSO nonlinearity Time [year] SSTA [°C]

ENSO Clivar Workshop, Paris, November 2010 ENSO - Asymmetry - Extreme Events Mean state Decadal 10 – 15 years Inter-decadal 20 – 50 years SHIFT Decadal modulation Inter-decadal modulation Seasonal Cycle NDH Residual Niño/Niña Phase locking - skewness[SST] - Var[SST] Low frequency High frequency Inter-decadal changes of ENSO nonlinearity

ENSO Clivar Workshop, Paris, November 2010 ENSO - Asymmetry - Extreme Events Mean state Decadal 10 – 15 years Inter-decadal 20 – 50 years SHIFT Decadal modulation Inter-decadal modulation Seasonal Cycle NDH Residual Niño/Niña Phase locking - skewness[SST] -  [SST] - Var[SST] Low frequency High frequency Inter-decadal changes of ENSO nonlinearity ? Phase locking ???

NINO4W 5°N 5°S 150°E190°E210°E270°E NINO3 NINO4W  Inter-decadal variability of nonlinearity as measured by [2-  ENSO Clivar Workshop, Paris, November 2010 [2 –  ] NINO3 [2 –  ] NINO4W Inter-decadal changes of ENSO nonlinearity estimation of  parameter on a 15-years running window

NINO4W 5°N 5°S 150°E190°E210°E270°E NINO3 NINO4W  Inter-decadal variability of nonlinearity as measured by [2-    Eastern and Western Tropical Pacific out of phase ENSO Clivar Workshop, Paris, November 2010 [2 –  ] NINO3 [2 –  ] NINO4W Inter-decadal changes of ENSO nonlinearity estimation of  parameter on a 15-years running window

NINO4W 5°N 5°S 150°E190°E210°E270°E NINO3 NINO4W  Inter-decadal variability of nonlinearity as measured by [2-    Eastern and Western Tropical Pacific out of phase   Do these long-term variations have the ability to influence seasonal SST variations and to rectify into the inter- decadal mean state ? ENSO Clivar Workshop, Paris, November 2010 [2 –  ] NINO3 [2 –  ] NINO4W Inter-decadal changes of ENSO nonlinearity estimation of  parameter on a 15-years running window

NINO4W 5°N 5°S 150°E190°E210°E270°E NINO3 NINO4W  Inter-decadal variability of nonlinearity as measured by [2-    Eastern and Western Tropical Pacific out of phase   Do these long-term variations have the ability to influence seasonal SST variations and to rectify into the inter- decadal mean state ? ENSO Clivar Workshop, Paris, November 2010 [2 –  ] NINO3 [2 –  ] NINO4W Inter-decadal changes of ENSO nonlinearity estimation of  parameter on a 15-years running window

ENSO Clivar Workshop, Paris, November 2010 NINO3 NINO4 West Inter-decadal changes of ENSO nonlinearity Mean[SST] Var[SST]  [SST]  Opposite behaviour between two consecutive inter-shifts periods

ENSO Clivar Workshop, Paris, November 2010 NINO3 NINO4 West Inter-decadal changes of ENSO nonlinearity Mean[SST] Var[SST]  [SST]  Opposite behaviour between two consecutive inter-shifts periods  Inter-decadal changes in amplitude and phase of mean seasonal cycle Anti Phase locking Phase locking Anti Phase locking

ENSO Clivar Workshop, Paris, November 2010 NINO3 NINO4 West Inter-decadal changes of ENSO nonlinearity  Opposite behaviour between two consecutive inter-shifts periods  Inter-decadal changes in amplitude and phase of mean seasonal cycle  Extreme Events residual can be rectified into the inter-decadal tropical Pacific mean state Anti Phase locking Phase locking Anti Phase locking Mean[SST] Var[SST]  [SST]

ENSO Clivar Workshop, Paris, November 2010 Inter-decadal changes of ENSO nonlinearity Time [year] SSTA [°C]

ENSO Clivar Workshop, Paris, November = Inter-decadal changes of ENSO nonlinearity ENSO low-frequency modulation due to its own irregularity SSTA [°C] Time [year]

ENSO Clivar Workshop, Paris, November 2010 ENSO - Asymmetry - Extreme Events Mean state Decadal 10 – 15 years Inter-decadal 20 – 50 years SHIFT Decadal modulation Inter-decadal modulation Seasonal Cycle NDH Residual Niño/Niña Phase locking - skewness[SST] -  [SST] - Var[SST] Low frequency High frequency Inter-decadal changes of ENSO nonlinearity Phase locking Extreme Events Residual Mechanism ?

ENSO Clivar Workshop, Paris, November 2010 ENSO - Asymmetry - Extreme Events Mean state Decadal 10 – 15 years Inter-decadal 20 – 50 years SHIFT Decadal modulation Inter-decadal modulation Seasonal Cycle NDH Residual Niño/Niña Phase locking - skewness[SST] -  [SST] - Var[SST] Low frequency High frequency Inter-decadal changes of ENSO nonlinearity Phase locking Extreme Events Residual Mechanism ? East-West see-saw Phase-locking alternation

ENSO Clivar Workshop, Paris, November 2010 Outline: 1. Measuring the nonlinearity of ENSO 2. Interactive feedback between ENSO irregularity and low-frequency variability 3. ENSO statistics in a warming climate 4. Conclusions, perspectives

ENSO Clivar Workshop, Paris, November 2010  IPCC database Models selection according to recent multimodel studies Model NameLength of PICTRL runLength of 2xCO 2 runLength of 4xCO 2 run 1BCCR-BCM CCCMA-CGCM3.1-t CSIRO-MK3.5 run CSIRO-MK3.5 run GFDL-CM GFDL-CM GISS-MODEL-E-H INM-CM MIROC3.2-HIRES MIROC3.2-MEDRES run MIROC3.2-MEDRES run MIROC3.2-MEDRES run MRI-CGCM2.3.2A UKMO-HadCM3 (run1) ENSO statistics in a warming climate

ENSO Clivar Workshop, Paris, November 2010  IPCC database Models selection according to recent multimodel studies and 3 scenarios Model NameLength of PICTRL runLength of 2xCO 2 runLength of 4xCO 2 run 1BCCR-BCM CCCMA-CGCM3.1-t CSIRO-MK3.5 run CSIRO-MK3.5 run GFDL-CM GFDL-CM GISS-MODEL-E-H INM-CM MIROC3.2-HIRES MIROC3.2-MEDRES run MIROC3.2-MEDRES run MIROC3.2-MEDRES run MRI-CGCM2.3.2A UKMO-HadCM3 (run1) ENSO statistics in a warming climate

  -stable parameters: 2xCO 2 – PICTRL :Boucharel et al. (2010), in rev. - Patterns of  indicate more drastic changes over Niño4 region. More nonlinear More linearMore negative asym More positive asym ENSO Clivar Workshop, Paris, November 2010  2xCO 2 –  PICTRL  2xCO 2 –  PICTRL Multi- Models Mean ENSO statistics in a warming climate

  -stable parameters: 2xCO 2 – PICTRL :Boucharel et al. (2010), in rev. - Patterns of  indicate more drastic changes over Niño4 region. - Zonal tripole pattern of  More nonlinear More linearMore negative asym More positive asym ENSO Clivar Workshop, Paris, November 2010  2xCO 2 –  PICTRL  2xCO 2 –  PICTRL Multi- Models Mean ENSO statistics in a warming climate

  -stable parameters: 2xCO 2 – PICTRL : Boucharel et al. (2010), in rev. - Patterns of  indicate more drastic changes over Niño4 region. - Zonal tripole pattern of   Intensification of Extreme El Niño events and nonlinearity over the western Pacific (« Modoki »)  Decrease of the traditional Cold Tongue El Niño. More nonlinear More linearMore negative asym More positive asym ENSO Clivar Workshop, Paris, November 2010  2xCO 2 –  PICTRL  2xCO 2 –  PICTRL Multi- Models Mean ENSO statistics in a warming climate

4xCO 2 – PICTRL:  4xCO 2 –  PICTRL  4xCO 2 –  PICTRL   -stable parameters: More nonlinear More linearMore negative asym More positive asym Multi- Models Mean ENSO Clivar Workshop, Paris, November 2010 ENSO statistics in a warming climate

4xCO 2 – PICTRL: - Same tendency than for 2xCO 2 – PICTRL. Patterns of change not altered - Treshold effect of climate change impact on the mean state  4xCO 2 –  PICTRL  4xCO 2 –  PICTRL   -stable parameters: More nonlinear More linearMore negative asym More positive asym Multi- Models Mean ENSO Clivar Workshop, Paris, November 2010 ENSO statistics in a warming climate

 Is it an artefact of one particular model ? ENSO Clivar Workshop, Paris, November 2010 ENSO statistics in a warming climate

 Is it an artefact of one particular model ? ENSO Clivar Workshop, Paris, November 2010 A Niño4WNiño3 Niño4W ENSO statistics in a warming climate

BCCR CCCMA CSIRO1 CSIRO2 GFDL0 GFDL1 GISSEh INM MIROC Hires MIROC Medres MRI UKMO Ensemble Mean ENSO Clivar Workshop, Paris, November 2010 Ensemble mean ENSO statistics in a warming climate  Wide range of behaviours in terms of nonlinearity

BCCR CCCMA CSIRO1 CSIRO2 GFDL0 GFDL1 GISSEh INM MIROC Hires MIROC Medres MRI UKMO Ensemble Mean ENSO Clivar Workshop, Paris, November 2010 Ensemble mean ENSO statistics in a warming climate  Wide range of behaviours in terms of nonlinearity  But 10/14 models exhibit a decrease in  ' trip with 70 % of them being confident at 95%

BCCR CCCMA CSIRO1 CSIRO2 GFDL0 GFDL1 GISSEh INM MIROC Hires MIROC Medres MRI UKMO Ensemble Mean ENSO Clivar Workshop, Paris, November 2010 ENSO statistics in a warming climate  Wide range of behaviours in terms of nonlinearity  But 10/14 models exhibit a decrease in  ' trip with 70 % of them being confident at 95%  Dominated by changes over the western tropical Pacific Ensemble Mean for NINO4W

ENSO Clivar Workshop, Paris, November 2010 ENSO statistics in a warming climate BCCR CCCMA CSIRO1 CSIRO2 GFDL0 GFDL1 GISSEh INM MIROC Hires MIROC Medres MRI UKMO Ensemble Mean  Confirmed by asymmetry changes  Decrease towards a strong negative asymmetry.  Reduction of asymmetry in the Cold Tongue (not shown).

ENSO Clivar Workshop, Paris, November 2010  Beyond inter- decadal variability, dominant phase- locking in the Eastern Pacific in 20th century and pre-industrial climate Mean[SST] Var[SST]  [SST] ENSO statistics in a warming climate

 Beyond inter- decadal variability, dominant phase- locking in the Eastern Pacific in 20th century and pre-industrial climate  In a warming climate, dominant phase-locking in the Western Pacific ENSO Clivar Workshop, Paris, November 2010 Var[SST]  [SST] ENSO statistics in a warming climate Mean[SST] Var[SST]  [SST]

 Beyond inter- decadal variability, dominant phase- locking in the Eastern Pacific in 20th century and pre-industrial climate  In a warming climate, dominant phase-locking in the Western Pacific Change in dynamic attractor, Bifurcation ? ENSO Clivar Workshop, Paris, November 2010 Var[SST]  [SST] ENSO statistics in a warming climate Mean[SST] Var[SST]  [SST]

ENSO Clivar Workshop, Paris, November 2010 Outline: 1. Measuring the nonlinearity of ENSO 2. Interactive feedback between ENSO irregularity and low-frequency variability 3. ENSO statistics in a warming climate 4. Conclusions, perspectives

  -stable laws allow defining relevant and mathematically defined metrics of high- statistical moments and therefore nonlinearity  High Statistical moments = important indicators of human-induced climate change (Timmermann, 1999). ENSO Clivar Workshop, Paris, November 2010 Conclusions

  -stable laws allow defining relevant and mathematically defined metrics of high- statistical moments and therefore nonlinearity  High Statistical moments = important indicators of human-induced climate change (Timmermann, 1999). ENSO Clivar Workshop, Paris, November 2010 Conclusions  Tropical Pacific system nonlinear by nature (Jin et al., 1994; Tziperman et al., 1994)  Nonlinearity associated with EE are involved in low-frequency modulation and may be responsible for abrupt transitions of tropical Pacific mean state (climate shifts).  Evidence of a significant change in nonlinearity patterns under greenhouse warming Changes not so sensitive between 2xCO2 and 4xCO2  Threshold / bifurcation crossed ?

  -stable laws allow defining relevant and mathematically defined metrics of high- statistical moments and therefore nonlinearity  High Statistical moments = important indicators of human-induced climate change (Timmermann, 1999). ENSO Clivar Workshop, Paris, November 2010 Conclusions  Tropical Pacific system nonlinear by nature (Jin et al., 1994; Tziperman et al., 1994)  Nonlinearity associated with EE are involved in low-frequency modulation and may be responsible for abrupt transitions of tropical Pacific mean state (climate shifts).  Evidence of a significant change in nonlinearity patterns under greenhouse warming Changes not so sensitive between 2xCO2 and 4xCO2  Threshold / bifurcation crossed ?  Apply this method on the new generations of CGCMs (CMIP5) even in PMIP (longer timeseries)  Identify the nonlinear processes associated with these changes (NDH, intra seasonal variability, TIW …?)

ENSO Clivar Workshop, Paris, November 2010 ENSO - Asymmetry - Extreme Events Mean state Decadal 10 – 15 years Inter-decadal 20 – 50 years SHIFT Decadal modulation Inter-decadal modulation Seasonal Cycle NDH Residual Niño/Niña Phase locking Low frequency High frequency ENSO modulation & Climate change Phase locking Extreme Events Residual Mechanism ? - skewness[SST] -  [SST] - Var[SST] El Niño Modoki – Cold Tongue East-West see-saw Phase-locking alternation Climate change Treshold Shift + tendency = Bifurcation ??

ENSO Clivar Workshop, Paris, November 2010 THANK YOU